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By
Gaxiola, Fernando; Melin, Patricia; Valdez, Fevrier; Castillo, Oscar
Show all (4)
1 Citations
This paper presents a new modular neural network architecture that is used to build a system for pattern recognition based on the iris biometric measurement of persons. In this system, the properties of the person iris database are enhanced with image processing methods, and the coordinates of the center and radius of the iris are obtained to make a cut of the area of interest by removing the noise around the iris. The inputs to the modular neural network are the processed iris images and the output is the number of the identified person. The integration of the modules was done with a type2 fuzzy integrator at the level of the sub modules, and with a gating network at the level of the modules.
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By
Sánchez, Daniela; Melin, Patricia; Castillo, Oscar
In this paper we propose a new model of a Modular Neural Network (MNN) with fuzzy integration based on granular computing. The topology and parameters of the model are optimized with a Hierarchical Genetic Algorithm (HGA). The model was applied to the case of human recognition to illustrate its applicability. The proposed method is able to divide the data automatically into sub modules, to work with a percentage of images and select which images will be used for training. We considered, to test this method, the problem of human recognition based on ear, and we used a database with 77 persons (with 4 images each person for this task).
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By
Gaxiola, Fernando; Melin, Patricia; Valdez, Fevrier; Castillo, Oscar
Show all (4)
2 Citations
In this paper a neural network learning method with type2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially the use of fuzzy weights. In this work an ensemble neural network of three neural networks and the use of average integration to obtain the final result is presented. The proposed approach is applied to a case of time series prediction to illustrate the advantage of using type2 fuzzy weights.
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By
Fazel Zarandi, Mohammad Hossein; Sadat Asl, Ali Akbar; Sotudian, Shahabeddin; Castillo, Oscar
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Intelligent scheduling covers various tools and techniques for successfully and efficiently solving the scheduling problems. In this paper, we provide a survey of intelligent scheduling systems by categorizing them into five major techniques containing fuzzy logic, expert systems, machine learning, stochastic local search optimization algorithms and constraint programming. We also review the application case studies of these techniques.
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By
Castillo, Oscar; Huesca, Gabriel; Valdez, Fevrier
15 Citations
Abstract
We describe in this paper the use of hierarchical genetic algorithms for fuzzy system optimization in intelligent control. In particular, we consider the problem of optimizing the number of rules and membership functions using an evolutionary approach. The hierarchical genetic algorithm enables the optimization of the fuzzy system design for a particular application. We illustrate the approach with the case of intelligent control in a medical application. Simulation results for this application show that we are able to find an optimal set of rules and membership functions for the fuzzy system.
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By
Astudillo, Leslie; Castillo, Oscar; Aguilar, Luis T.; Martínez, Ricardo
Show all (4)
11 Citations
This paper focuses on the control of wheeled mobile robot under bounded torque disturbances. Hybrid tracking controller for the mobile robot was developed by considering its kinematic model and EulerLagrange dynamics. The procedure consist in minimizing the stabilization error of the kinematic model through genetic algorithm approach while attenuation to perturbed torques is made through type2 Fuzzy Logic Control (FLC) via backstepping methodology. Type2 fuzzy logic is proposed to synthesize the controller for the overall system which is claimed to be a robust tool for related applications. The theoretical results are illustrated through computer simulations of the closedloop system.
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By
Naredo, Enrique; Castillo, Oscar
7 Citations
We describe the use of Ant Colony Optimization (ACO) for the ball and beam control problem, in particular for the problem of tuning a fuzzy controller of the Sugeno type. In our case study the controller has four inputs, each of them with two membership functions; we consider the intersection point for every pair of membership functions as the main parameter and their individual shape as secondary ones in order to achieve the tuning of the fuzzy controller by using an ACO algorithm. Simulation results show that using ACO and coding the problem with just three parameters instead of six, allows us to find an optimal set of membership function parameters for the fuzzy control system with less computational effort needed.
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By
Sánchez, Daniela; Melin, Patricia; Castillo, Oscar
In this paper, a new method for fuzzy inference system optimization is proposed. The optimization consists in find the optimal parameters of fuzzy inference system used to combine the responses of modular neural networks using a hierarchical genetic algorithm. The optimized parameters are: type of fuzzy logic (type1 and interval type2), type of system (Mamdani or Sugeno), type of membership functions, number of membership functions in each variable (inputs and output), their parameters and the consequents of the fuzzy rules. Four benchmark databases are used to test the proposed method where, each database is a different biometric measure (face, iris, ear and voice) and each database is learned by a modular neural network. The main objective of the fuzzy inference system is to combine the different responses of the modular neural network and achieve final good results even when one (o more) biometric measure has individually a bad result. The results obtained in a previous work are used to compare with the results obtained in this paper.
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By
Sánchez, Daniela; Melin, Patricia; Castillo, Oscar; Valdez, Fevrier
Show all (4)
1 Citations
In this paper a new model of a Modular Neural Network (MNN) with fuzzy integration based on granular computing is proposed. The topology and parameters of the MNN are optimized with a Hierarchical Genetic Algorithm (HGA). The proposed method can divide the data automatically into sub modules or granules, chooses the percentage of images and selects which images will be used for training. The responses of each sub module are combined using a fuzzy integrator, the number of the fuzzy integrators will depend of the number of sub modules or granules that the MNN has at a particular moment. The method was applied to the case of human recognition to illustrate its applicability with good results.
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By
Valdez, Fevrier; Melin, Patricia; Castillo, Oscar
5 Citations
We describe in this paper an approach for mathematical function optimization using fuzzy logic for parameter tuning combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs). The proposed method combines the advantages of PSO and GA to give us an improved FPSO+FGA hybrid method. Fuzzy logic is helpful to find the optimal parameters in PSO and GA in the best way possible. Also, with the tuning of parameters based on fuzzy logic it is possible to balance the exploration and exploitation of the proposed method. The hybrid method is called FPSO+FGA and was tested with a set of benchmark mathematical functions.
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By
Valdez, Fevrier; Melin, Patricia; Castillo, Oscar
1 Citations
We describe in this paper a new hybrid approach for mathematical function optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved PSO+GA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid PSO+GA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The new hybrid PSO +GA method is shown to be superior than the individual evolutionary methods.
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By
Valdez, Fevrier; Melin, Patricia; Castillo, Oscar
7 Citations
We describe in this paper a Parallel Particle Swarm Optimization (PPSO) method with dynamic parameter adaptation to optimize complex mathematical functions. Fuzzy Logic is used to adapt the parameters of the PSO in the best way possible. The PPSO is shown to be superior to the individual evolutionary methods on the set of benchmark functions.
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By
Lopez, Miguel; Melin, Patricia; Castillo, Oscar
8 Citations
We describe in this paper a new method for response integration in ensemble neural networks with Type1 Fuzzy Logic and Type2 Fuzzy Logic using Genetic Algorithms (GA’s) for optimization. In this paper we consider pattern recognition with ensemble neural networks for the case of fingerprints. An ensemble neural network of three modules is used. Each module is a local expert on person recognition based on their biometric measure (Pattern recognition for fingerprints). The Response Integration method of the ensemble neural networks has the goal of combining the responses of the modules to improve the recognition rate of the individual modules. Using GA’s to optimize the Membership Functions of The Type1 Fuzzy System and Type2 Fuzzy System we can improve the results of the fuzzy systems. We show in this paper the results of a type2 approach for response integration that improves performance over the type1 logic approaches.
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By
Montiel, Oscar; Castillo, Oscar; Melin, Patricia; Sepúlveda, Roberto
Show all (4)
Abstract
There exists no standard method for obtaining a nonlinear inputoutput model using external dynamic approach. In this work, we are using an evolutionary optimization method for estimating the parameters of an NFIR model using the Wiener model structure. Specifically we are using a Breeder Genetic Algorithm (BGA) with fuzzy recombination for performing the optimization work. We selected the BGA since it uses real parameters (it does not require any string coding), which can be manipulated directly by the recombination and mutation operators. For training the system we used amplitude modulated pseudo random binary signal (APRBS). The adaptive system was tested using sinusoidal signals.
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By
Garibaldi, Julian; Barreras, Azucena; Castillo, Oscar
Abstract
problem of Offline PointtoPoint Autonomous Mobile Robot Path Planning. The problem consist of generating “valid” paths or trajectories, for an Holonomic Robot to use to move from a starting position to a destination across a flat map of a terrain, represented by a two dimensional grid, with obstacles and dangerous ground that the Robot must evade. This means that the GA optimizes possible paths based on two criteria: length and difficulty. This paper describes the use of a Genetic Algorithm (GA) for the
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By
Hidalgo, Denisse; Melin, Patricia; Licea, Guillerrno; Castillo, Oscar
Show all (4)
6 Citations
We describe in this paper a new evolutionary method for the optimization of a modular neural network for multimodal biometry The proposed evolutionary method produces the best architecture of the modular neural network (number of modules, layers and neurons) and fuzzy inference systems (memberships functions and rules) as fuzzy integration methods. The integration of responses in the modular neural network is performed by using type1 and type2 fuzzy inference systems.
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By
Montiel, Oscar; Castillo, Oscar
Abstract
In this paper we are proposing a novel fuzzy method that can handle imperfect knowledge in a broader way than Intuitionistic fuzzy logic does (IFL). This fuzzy method can manage noncontradictory, doubtful, and contradictory information provided by experts, providing a mediated solution, so we called it Mediative Fuzzy Logic (MFL). We are comparing results of MFL, with IFL and traditional Fuzzy logic (FL).
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By
Martinez, Cristina; Castillo, Oscar; Montiel, Oscar
6 Citations
In this paper we show some of the results that we obtain with different evolutionary methods on a Mamdani Fuzzy Inference System (FIS); we work with Hierarchical Genetic Algorithms (HGA) and the Ant Colony Optimization (ACO), the fuzzy inference system controls a benchmark problem which is “The Ball and Beam” system, optimizing the fuzzy rules of the system. Firs, we work to optimize the FIS that is structured by two inputs (the error and the derived error), an output (the angle of the beam so that we can get the ball position on it); and the 44 fuzzy rules that we used to be reduced with the evolutionary methods (HGA, ACO), so that we could make the comparisons between them via average and standard deviation, and concluding with the best evolutionary method for a fuzzy system optimization control problem.
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By
Caraveo, Camilo; Valdez, Fevrier; Castillo, Oscar
6 Citations
In this paper the application of a new method of bioinspired optimization based on the selfdefense mechanism of plants is presented. Through time the planet has gone through changes, so plants have had to adapt to these changes and adopt new techniques to defend from natural predators (herbivores). Several works have shown that plants have mechanisms of selfdefense to protect themselves from predators. When the plants detect the presence of invading organisms this triggers a series of chemical reactions that are released to air and attract natural predators of the invading organism [1, 9, 10]. For the development of this algorithm we consider as a main idea the predator prey mathematical model of Lotka and Volterra, where two populations are considered and the objective is to maintain a balance between the two populations.
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By
Cervantes, Leticia; Castillo, Oscar; Melin, Patricia; Valdez, Fevrier
Show all (4)
4 Citations
In this paper, simulation results with type1 fuzzy systems and a type2 fuzzy granular approach for intelligent control of nonlinear dynamical plants are presented. First, the proposed method for intelligent control using a type2 fuzzy granular approach is described. Then, the proposed method is illustrated with the benchmark case of three tank water level control. Finally, a comparison between a type1 fuzzy system and the type2 fuzzy granular system for water control is presented.
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By
Valdez, Fevrier; Melin, Patricia; Castillo, Oscar
This paper describes a hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results, the proposed method is called FPSO+FGA. The new hybrid FPSO+FGA approach is compared with the Simulated Annealing (SA), PSO, GA, Pattern Search (PS) methods with a set of benchmark mathematical functions.
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By
MartinezSoto, Ricardo; Castillo, Oscar; Aguilar, Luis T.; Melin, Patricia
Show all (4)
11 Citations
In this paper we apply to Bioinspired and evolutionary optimization methods to design fuzzy logic controllers (FLC) to minimize the steady state error of linear systems. We test the optimal FLC obtained by the genetic algorithms and the PSO applied on linear systems using benchmark plants. The bioinspired and the evolutionary methods are used to find the parameters of the membership functions of the FLC to obtain the optimal controller. Simulation results are obtained with Simulink showing the feasibility of the proposed approach.
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By
Lopez, Miguel; Melin, Patricia; Castillo, Oscar
We describe in this paper a new method for response integration in ensemble neural networks with Type1 Fuzzy Logic and Type2 Fuzzy Logic using Genetic Algorithms (GA’s) for optimization. In this paper we consider pattern recognition with ensemble neural networks for the case of fingerprints to the test proposed method of response integration. An ensemble neural network of three modules is used. Each module is a local expert on person recognition based on their biometric measure (Pattern recognition for fingerprints). The Response Integration method of the ensemble neural networks has the goal of combining the responses of the modules to improve the recognition rate of the individual modules. First we use GA’s to optimize the fuzzy rules of The Type1 Fuzzy System and Type2 Fuzzy System to test the proposed method of response integration and after using GA’s to optimize the membership function of The Type1 Fuzzy Logic and Type2 Fuzzy logic to test the proposed method of response integration and finally show the comparison of the results between these methods. We show in this paper a comparative study of fuzzy methods for response integration and the optimization of the results of a type2 approach for response integration that improves performance over the type1 logic approaches.
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By
Cervantes, Leticia; Castillo, Oscar; Melin, Patricia
14 Citations
In this paper we present simulation results that we have at this moment with a new approach for intelligent control of nonlinear dynamical plants. First we present the proposed approach for intelligent control using a hierarchical modular architecture with type2 fuzzy logic used for combining the outputs of the modules. Then, the approach is illustrated with two cases: aircraft control and shower control and in each problem we explain its behavior. Simulation results of the two case show that proposed approach has potential in solving complex control problems.
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By
Pérez, Jonathan; Valdez, Fevrier; Castillo, Oscar
3 Citations
We describe in this paper a new approach to enhance the bat algorithm using a fuzzy system to dynamically adapt its parameters. The original method is compared with the proposed method and also compared with genetic algorithms, providing a more complete analysis of the effectiveness of the bat algorithm. Simulation results on a set of benchmark mathematical functions show that the fuzzy bat algorithm outperforms the traditional bat algorithm and genetic algorithms.
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By
Hidalgo, Denisse; Melin, Patricia; Castillo, Oscar
1 Citations
In this paper we describe a method for the optimization of type2 fuzzy systems based on the level of uncertainty considering three different cases to reduce the complexity problem of searching the solution space. The proposed method produces the best fuzzy inference systems for particular applications based on a genetic algorithm. We apply a Genetic Algorithm to find the optimal type2 fuzzy system dividing the search space in three subspaces. We show the comparative results obtained for the benchmark problems.
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By
Rodríguez, Luis; Castillo, Oscar; Soria, José
The main goal of this paper is to present a general study of the Grey Wolf Optimizer algorithm. We perform tests to determine in the first part which parameters are candidates to be dynamically adjusted and in the second stage to determine which are the parameters that have the greatest effect in the performance of the algorithm. We also present a justification and results of experiments as well as the benchmark functions that were used for the tests that are presented. In addition we are presenting a simple fuzzy system with the results obtained based on this general study.
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By
Mendoza, Olivia; Melin, Patricia; Castillo, Oscar; Licea, Guillermo
Show all (4)
19 Citations
The combination of Soft Computing techniques allows the improvement of intelligent systems with different hybrid approaches. In this work we consider two parts of a Modular Neural Network for image recognition, where a Type2 Fuzzy Inference System (FIS 2) makes a great difference. The first FIS 2 is used for feature extraction in training data, and the second one to find the ideal parameters for the integration method of the modular neural network. Once again Fuzzy Logic is shown to be a tool that can help improve the results of a neural system, when facilitating the representation of the human perception.
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By
Montiel, Oscar; Castillo, Oscar; Melin, Patricia; Sepúlveda, Roberto
Show all (4)
Abstract
There exists no standard method for obtaining a nonlinear inputoutput model using external dynamic approach. In this work, we are using an evolutionary optimization method for estimating the parameters of an NFIR model using the Wiener model structure. Specifically we are using a Breeder Genetic Algorithm (BGA) with fuzzy recombination for performing the optimization work. We selected the BGA since it uses real parameters (it does not require any string coding), which can be manipulated directly by the recombination and mutation operators. For training the system we used amplitude modulated pseudo random binary signal (APRBS). The adaptive system was tested using sinusoidal signals.
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By
Castillo, Oscar; Melin, Patricia; Valdez, Fevrier
A review of the optimization methods used in the design of type2 fuzzy systems, which are relatively novel models of imprecision, is presented in this paper. The main aim of the work is to study the basic reasons for optimizing type2 fuzzy systems for solving problems different areas of application. Recently, natureinspired methods have emerged as powerful optimization algorithms for solving complex problems. In the case of designing type2 fuzzy systems for particular applications, the use of natureinspired optimization methods have helped in the complex task of finding the appropriate parameter values and structure of the fuzzy systems. In this paper, we consider the application of genetic algorithms, particle swarm optimization and ant colony optimization as three different paradigms that help in the design of optimal type2 fuzzy systems. A comparison of the different optimization methods for the case of designing type2 fuzzy systems is also offered.
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By
Castro, Juan R.; Castillo, Oscar; Melin, Patricia; RodríguezDíaz, Antonio
Show all (4)
26 Citations
This paper presents the development and design of a graphical user interface and a command line programming Toolbox for construction, edition and simulation of Interval Type2 Fuzzy Inference Systems. The Interval Type2 Fuzzy Logic System (IT2FLS) Toolbox, is an environment for interval type2 fuzzy logic inference system development. Tools that cover the different phases of the fuzzy system design process, from the initial description phase, to the final implementation phase, constitute the Toolbox. The Toolbox’s best qualities are the capacity to develop complex systems and the flexibility that allows the user to extend the availability of functions for working with the use of type2 fuzzy operators, linguistic variables, interval type2 membership functions, defuzzification methods and the evaluation of Interval Type2 Fuzzy Inference Systems.
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By
Castillo, Oscar; Huesca, Gabriel; Valdez, Fevrier
1 Citations
Abstract
We describe in this paper the use of hierarchical genetic algorithms for fuzzy system optimization in intelligent control. In particular, we consider the problem of optimizing the number of rules and membership functions using an evolutionary approach. The hierarchical genetic algorithm enables the optimization of the fuzzy system design for a particular application. We illustrate the approach with the case of intelligent control in a medical application. Simulation results for this application show that we are able to find an optimal set of rules and membership functions for the fuzzy system.
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By
MartínezMarroquín, Ricardo; Castillo, Oscar; Soria, José
12 Citations
In this paper we describe the application of a Particle Swarm Optimization (PSO) algorithm as a method of optimization for membership functions’ parameters of a fuzzy logic controller (FLC) in order to find the optimal intelligent controller for an Autonomous Wheeled Mobile Robot. Simulations results show that PSO is able to optimize the tipe1 and type2 FLCs for this application.
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By
AmadorAngulo, Leticia; Castillo, Oscar
4 Citations
A new fuzzy Bee Colony Optimization (FBCO) algorithm with dynamic adaptation in the alpha and beta parameters using an Interval Type2 Fuzzy Logic System is presented in this paper. The Bee Colony Optimization metaheuristic belongs to the class of NatureInspired Algorithms. The objective of the work is based on the use of Interval Type2 Fuzzy Logic to find the best Beta and Alpha parameter values in BCO. We use BCO specifically for tuning membership functions of the fuzzy controller for stability of the trajectories in a mobile robot. We implemented the IAE and MSE metrics as performance metrics of control. We added perturbations in the model with the pulse generator so that the Interval Type2 Fuzzy Logic System is better analyzed under uncertainty and to verify that the FBCO shows better results than the traditional BCO.
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By
LealRamirez, Cecilia; Castillo, Oscar; RodriguezDiaz, Antonio
2 Citations
As part of the research about the applicability of the Fuzzy Cellular Models (FCM) to simulate complex dynamics ecological systems, this paper presents an Interval Type2 Fuzzy Cellular Model (IT2FCM) applied to the dynamics of a unispecific population. In ecology is known that in the dynamics of all population, the reproduction, mortality and emigration rates are not constants, and its variability is induced by a combination of environment factors. All kind of populations that are living together in a determinate place, and the physical factors which they interact with, compose a community. Each population o physical factor inside has its own dynamics, which also presents uncertainty given by combined effects among other environment factors inside the same community. In our model this uncertainty is represented by interval type2 fuzzy sets, with the goal of show whether the trajectories described by the dynamics of the population present a better stability in the time and space. The validation of the model was made within a comparative frame using the results of another research where a FCM were used to describe the dynamics of a population.
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By
Soto, Jesus; Melin, Patricia; Castillo, Oscar
1 Citations
This paper describes the Particle Swarm Optimization of the Fuzzy integrators in Ensembles of ANFIS (adaptive neurofuzzy inferences systems) models for the prediction time series. A chaotic system is considered in this work, which is the MackeyGlass time series, that is generated from a model is in the form of differential equations. This benchmark time series is used to test of performance of the proposed optimization of the fuzzy integrators in ensemble architecture. We used interval type2 and type1 fuzzy systems to integrate the output (forecast) of each Ensemble of ANFIS models. Particle Swarm Optimization (PSO) was used for the optimization of membership function parameters of each fuzzy integrator. In the experiments we optimized Gaussian, Generalized Bell and Triangular membership functions parameters for each of the fuzzy integrators. Simulation results show the effectiveness of the proposed approach. Therefore, a comparison was made against another recent work to validate the performance of the proposed model.
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By
Melin, Patricia; Mancilla, Alejandra; Lopez, Miguel; Solano, Daniel; Soto, Miguel; Castillo, Oscar
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2 Citations
We describe in this paper the evolution of modular neural networks using hierarchical genetic algorithms for pattern recognition. Modular Neural Networks (MNN) have shown significant learning improvement over single Neural Networks (NN). For this reason, the use of MNN for pattern recognition is well justified. However, network topology design of MNN is at least an order of magnitude more difficult than for classical NNs. We describe in this paper the use of a Hierarchical Genetic Algorithm (HGA) for optimizing the topology of each of the neural network modules of the MNN. The HGA is clearly needed due to the fact that topology optimization requires that we are able to manage both the layer and node information for each of the MNN modules. Simulation results prove the feasibility and advantages of the proposed approach.
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By
Cázarez, Nohé; Castillo, Oscar; Aguilar, Luís; Cárdenas, Selene
Show all (4)
2 Citations
Abstract
Stability is one of the more important aspects in the traditional knowledge of Automatic Control. Type2 Fuzzy Logic is an emerging and promising area for achieving Intelligent Control (in this case, Fuzzy Control). In this work we use the Fuzzy Lyapunov Synthesis as proposed by Margaliot [11] to build a Lyapunov Stable Type1 Fuzzy Logic Control System, and then we make an extension from a Type1 to a Type2 Fuzzy Logic Control System, ensuring the stability on the control system and proving the robustness of the correponding fuzzy controller.
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By
Montiel, Oscar; Castillo, Oscar; Melin, Patricia; Sepulveda, Roberto
Show all (4)
1 Citations
In this work we are optimizing an adaptive finite impulse response filter applied to the problem of system identification. We are proposing a breeder genetic algorithm (BGA) for performing the parametric search in highly multimoldal landscapes since in this kind of filters there exits epistiasis. The results of BGA were compared to the traditional genetic algorithm, and we found that the BGA was clearly superior (in accuracy) in most of the cases. We used the statistical least mean squared for validating the results. We suggest to hybridized both methods for real world applications.
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By
Sanchez, Mauricio A.; Castillo, Oscar; Castro, Juan R.
This paper proposes a new method for directly discovering the uncertainty from a sample of discrete data, which is then used in the formation of an Interval Type2 Fuzzy Inference System. A Coefficient of Variation is used to measure the uncertainty on a finite sample of discrete data. Based on the maximum possible coverage area of the Footprint of Uncertainty of Gaussian membership functions, with uncertainty on the standard deviation, which then are modified according to the found index values, obtaining all antecedents in the process. Afterwards, the Cuckoo Search algorithm is used to optimize the Interval Sugeno consequents of the Fuzzy Inference System. Some sample datasets are used to measure the output interval coverage.
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By
Valdez, Fevrier; Melin, Patricia; Castillo, Oscar
2 Citations
We describe in this paper a new hybrid approach for mathematical function optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved PSO+GA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid PSO+GA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The new hybrid PSO+GA method is shown to be superior than the individual evolutionary methods.
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By
Méndez, Eduardo; Castillo, Oscar; Soria, José; Melin, Patricia; Sadollah, Ali
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1 Citations
This paper describes the enhancement of the Water Cycle Algorithm (WCA) using a fuzzy inference system to adapt its parameters dynamically. The original WCA is compared regarding performance with the proposed method called Water Cycle Algorithm with Dynamic Parameter Adaptation (WCADPA). Simulation results on a set of wellknown test functions show that the WCA can be improved with a fuzzy dynamic adaptation of the parameters.
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By
Montiel, Oscar; Castillo, Oscar; Melin, Patricia; Sepulveda, Roberto
Show all (4)
1 Citations
Summary
In this chapter, we are proposing an approach for integrating evolutionary computation applied to the problem of system identification in the wellknown statistical signal processing theory. Here, some mathematical expressions are developed in order to justify the learning rule in the adaptive process when a Breeder Genetic Algorithm is used as the optimization technique. In this work, we are including an analysis of errors, energy measures, and stability.
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By
Cárdenas, Selene L.; Castillo, Oscar; Aguilar, Luis T.; Cázarez, Nohé
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Abstract
We develop a tracking controller for the dynamic model of unicycle mobile robot by integrating a kinematic controller and a torque controller based on Fuzzy Logic Theory. Computer simulations are presented confirming the performance of the tracking controller and its application to different navigation problems.
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By
Muñoz, Ricardo; Castillo, Oscar; Melin, Patricia
5 Citations
In this paper we describe a Modular Neural Network (MNN) with fuzzy integration for face, fingerprint and voice recognition. The proposed MNN architecture defined in this paper consists of three modules; face, fingerprint and voice. Each of the mentioned modules is divided again into three sub modules. The same information is used as input to train the sub modules. Once we have trained and tested the MNN modules, we proceed to integrate these modules with a fuzzy integrator. In this paper we demonstrate that using MNNs for face, fingerprint and voice recognition integrated with a fuzzy integrator is a good option to solve pattern recognition problems.
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By
Castillo, Oscar; Melin, Patricia
Article Outline
Glossary
Definition of the Subject
Introduction
Genetic Algorithm for Optimization
Evolution of Fuzzy Systems
Application to Anesthesia Control
Application to the Control of the Bar and Ball System
Hierarchical Genetic Algorithms for Neural Networks
Experimental Results for Time Series Prediction
Conclusions
Future Directions
Bibliography
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By
Peraza, Cinthia; Valdez, Fevrier; Castillo, Oscar; Melin, Patricia
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A fuzzy harmony search algorithm (FHS) is presented in this paper. This method uses a fuzzy system for dynamic adaptation of the harmony memory accepting (HMR) parameter along the iterations, and in this way achieving control of the intensification and diversification of the search space. This method was previously applied to classic benchmark mathematical functions with different number of dimensions. However, in this case we decided to apply the proposed FHS to benchmark mathematical problems provided by the CEC 2015 competition, which are unimodal, multimodal, hybrid and composite functions to check the efficiency for the proposed method. A comparison is presented to verify the results obtained with respect to the original harmony search algorithm and fuzzy harmony search algorithm.
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By
Ochoa, Patricia; Castillo, Oscar; Soria, José; CortesAntonio, Prometeo
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The main contribution of this paper is the use of a new concept of type reduction in type2 fuzzy systems for improving performance in differential evolution algorithm. The proposed method is an analytical approach using an approximation to the Continuous KarnikMendel (CEKM) method, and in this way the computational evaluation cost of the Interval Type 2 Fuzzy System is reduced. The performance of the proposed approach was evaluated with seven reference functions using the Differential Evolution algorithm with a crossover parameter that is dynamically adapted with the proposed methodology.
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By
O, David; Castillo, Oscar; Astudillo, Leslie; Soria, José
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1 Citations
In this paper, a Fuzzy Chemical Reaction Algorithm (FCRA) is proposed. In order to overcome the problems of the basic Chemical Reaction Algorithm (CRA), we improve the CRA by proposing a FCRA that takes into account the diversity of the population. Comparative experimental results with benchmark functions show that our proposed method performs much better than the original algorithm in problems with many dimensions.
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By
Melin, Patricia; Pulido, Martha; Castillo, Oscar
1 Citations
This paper describes the design of ensemble neural networks using Particle Swarm Optimization (PSO) for time series prediction with Type1 and Type2 Fuzzy Integration. The time series that is being considered in this work is the MackeyGlass benchmark time series. Simulation results show that the ensemble approach produces good prediction of the MackeyGlass time series.
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By
Lagunes, Marylu L.; Castillo, Oscar; Valdez, Fevrier; Soria, Jose; Melin, Patricia
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This paper describes the comparison of dynamic adjustment parameters in the firefly algorithm using type1 and type2 fuzzy logic for the optimization of a fuzzy controller. The adjustment is performed to improve the behavior of the method. Fuzzy systems use fuzzy sets by defining membership functions, which indicate how much an element belongs to the fuzzy set. Type2 fuzzy logic assigns degrees of belonging that are fuzzy and this can be viewed as an extension of type1 fuzzy logic. The Firefly algorithm has 3 main parameters Beta, Gamma and Alpha with a range of 0 to 1 each, which need to the dynamically adjusted to improve the performance of the algorithm.
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By
Ochoa, Patricia; Castillo, Oscar; Soria, José
The study of metaheuristics has become an important area for research, these metaheuristics contain parameters and the literature provides us with a range of values in which the algorithm can have good results. For this paper we propose to use the Differential Evolution algorithm combined with fuzzy logic to enable having dynamic crossover parameter, and to complement this work we include the diversity variable based on Euclidean distance, which will help us to know if the individuals of the population are separated or near in the search space in other words is the exploration and the exploitation in the search space, and for this article we work with two types of Simple Multimodal and Hybrid functions belonging to set of CEC 2015 benchmark functions.
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By
Bernal, Emer; Castillo, Oscar; Soria, José; Valdez, Fevrier
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In this work, a fuzzy method for dynamic adjustment of parameters in galactic swarm optimization is presented. Galactic swarm optimization is based on the movement of stars and galaxies in the universe, as well as their attractive influence allowing the use of multiple cycles of exploration and exploitation to solve complex optimization problems. It has been observed in the literature that the utilization of fuzzy systems for dynamic adjustment of parameters in metaheuristic algorithms produces good results when compared to using fixed parameters in the algorithms. In this work, the adjustment of the c_{3} and c_{4} parameters is made through the use of fuzzy systems because these parameters have a significant role in the operation of galactic swarm optimization. We tested the fuzzy approach with a set of benchmark mathematical functions and with the fuzzy controller of the water tank problem to measure the performance. Finally, a comparison of the results is presented among the proposed method and other metaheuristics.
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By
SalazarTejeda, Pedro Antonio; Melin, Patricia; Castillo, Oscar
13 Citations
In this paper, we describe an application of biometric recognition that is structured basically with three inputs: the hand geometry, voice and image. The hand geometry is given by an image of “the palm” of the hand with a 480x640 size which is preprocessed with a feature extraction that uses computer vision techniques and with certain features we recognize the individual. After that we preprocessed the image and get some variables as the fingers, palm, wrist, also a segment of the palm; they appear to be from a with a fuzzy system that will tell us how much they seemed to a certain person, comparing each variable given by the preprocessing of the image according to the data base that its already stored (all the images of the individuals, voice, etc.).
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By
Bernal, Emer; Castillo, Oscar; Soria, José
2 Citations
This paper applies the imperialist competitive algorithm (ICA) to benchmark mathematical functions with the original method to analyze and perform a study of the variation of the results obtained with the ICA algorithm as we vary the parameters manually for 4 mathematical functions. The results demonstrate the efficiency of the algorithm to optimization problems and give us the pattern for future work in dynamically adapting these parameters.
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By
Madera, Quetzali; Garcia, Mario; Castillo, Oscar
1 Citations
The description of a product or an ad’s text can be rewritten in many ways if other text fragments similar in meaning substitute different words or phrases. A good selection of words or phrases, composing an ad, is very important for the creation of an advertisement text, as the meaning of the text depends on this and it affects in a positive or a negative way the interest of the possible consumers towards the advertised product. In this paper we present a method for the optimization of advertisement texts through the use of interactive evolutionary computing techniques. The EvoSpace platform is used to perform the evolution of a text, resulting in an optimized text, which should have a better impact on its readers in terms of persuasion.
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By
Ramírez, Cecilia Leal; Castillo, Oscar
1 Citations
At present time, new advances in the generation of computational models can be applied to improve tasks in different areas of research. The hybrid computational models can be considered as new advances in science. In the present work a hybrid model has been proposed on the basis of a cellular automata and fuzzy logic to simulate, in space and time, the dynamics of a population structured by ages and where the changes in the levels of the biomass are induced by a stochastic variation of the environment. The model can be used as computational tool in the area of the Biology to describe and quantify the changes that continuously occurs in the population, knowing not only their size and its structure, but the form and the intensity in which it changes and renews.
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By
Lizárraga, Gabriel; Sepúlveda, Roberto; Montiel, Oscar; Castillo, Oscar
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4 Citations
Nowadays, there is an increasing interest in using FPGA devices to design digital controller, and a growing interest in control systems based on fuzzy logic where the Defuzzification stage is of primordial importance. In this work we are presenting the design, modeling and simulation of a fixed point defuzzification VHDL method. The modeling and simulation of this stage is realized in Simulink through the Xilinx System Generator, and a second inference system was implemented with Matlab code. Comparative analysis of both systems and result are shown.
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By
CazarezCastro, Nohé R.; Aguilar, Luis T.; Castillo, Oscar; Cárdenas, Selene
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4 Citations
A Fuzzy Logic Control System is designed to achieve the output regulation for a servomechanism with backlash. The problem is to design a fuzzy controller to obtain the closedloop system in which the load of the driver is regulated to a desired position. The provided servomotor position as the only measurement available for feedback, the proposed method is far from trivial because of nonminimum phase properties of the system. Simulation results illustrate the effectiveness of the closedloop system.
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By
Hidalgo, Denisse; Castillo, Oscar; Melin, Patricia
15 Citations
We describe in this paper a comparative study of Fuzzy Inference Systems as methods of integration in modular neural networks (MNN’s) for multimodal biometry. These methods of integration are based on type1 and type2 fuzzy logic. Also, the fuzzy systems are optimized with simple genetic algorithms. First, we considered the use of type1 fuzzy logic and later the approach with type2 fuzzy logic. The fuzzy systems were developed using genetic algorithms to handle fuzzy inference systems with different membership functions, like the triangular, trapezoidal and Gaussian; since these algorithms can generate the fuzzy systems automatically. Then the response integration of the modular neural network was tested with the optimized fuzzy integration systems. The comparative study of type1 and type2 fuzzy inference systems was made to observe the behavior of the two different integration methods f modular neural networks for multimodal biometry.
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By
Millán, Ismael; Montiel, Oscar; Sepúlveda, Roberto; Castillo, Oscar
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2 Citations
It is presented a novel hybrid controller that combines the benefits of classical controllers and fuzzy logic to improve the system response in tracking. The design was developed in VHDL for a posterior FPGA implementation. The code was simulated using Simulink and Xilinx System Generator (XSG) that allows to simulate the code of the final FPGA target. Several comparative experiments in softreal time were conducted using a geared DC motor and the results are commented.
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By
Olivas, José Á.; Sepúlveda, Roberto; Montiel, Oscar; Castillo, Oscar
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7 Citations
There exists an increasing interest in the field of digital intelligent systems, being one of the current research target the computational efficiency. This work presents the implementation of a fuzzy inference system faced to achieve high performance computations since the highly flexibility that a specific tailored FPGA implementation can offer to parallelize processes. A methodology to simulate and validate the inference engine developed in VHDL is given. Improvements over an exciting inference engine are proposed. The resulting code can be implemented in specific application hardware.
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By
Garza, Arnulfo Alanis; Castillo, Oscar; Valdez, José Mario García
Intelligent Agents have originated a lot discussion about what they are, and how they are a different from general programs. We describe in this paper a new paradigm for intelligent agents, This paradigm helped us deal with failures in an independent and efficient way. We proposed these types of agents to treat the system in a hierarchical way. The Agent Node is also described. A new method to visualize fault tolerant system (FTS) is proposed in this paper with the incorporation of intelligent agents, which as they grow and specialize create the Multi Agent System (MAS).The communications diagrams of the each of the agents is described in diagrams of transaction of states.
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By
Martínez, Ricardo; Castillo, Oscar; Aguilar, Luis T.
4 Citations
We describe a tracking controller for the dynamic model of a unicycle mobile robot by integrating a kinematics and a torque controller based on Interval Type2 Fuzzy Logic Theory and Genetic Algorithms. Computer simulations are presented confirming the performance of the tracking controller and its application to different navigation problems.
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By
Martinez, Alma I.; Castillo, Oscar; Garcia, Mario
2 Citations
This paper describes a comparative study of type1 and type2 fuzzy controllers that are optimized using hierarchical genetic algorithms. Fuzzy controllers of Sugeno and Mamdani form are studied. The hierarchical genetic algorithms optimize the membership functions and the rules of the fuzzy controllers.
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By
Castro, Juan R.; Castillo, Oscar; Melin, Patricia; RodríguezDíaz, Antonio
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5 Citations
In this work, a class of Interval Type2 Fuzzy Neural Networks (IT2FNN) is proposed, which is functionally equivalent to interval type2 fuzzy inference systems. The computational process envisioned for fuzzy neural systems is as follows: it starts with the development of an ”Interval Type2 Fuzzy Neuron”, which is based on biological neural morphologies, followed by the learning mechanisms. We describe how to decompose the parameter set such that the hybrid learning rule of adaptive networks can be applied to the IT2FNN architecture for the TakagiSugenoKang reasoning.
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By
Mendivil, Salvador González; Castillo, Oscar; Melin, Patricia
2 Citations
This paper considers the application of parallel genetic algorithms to the optimization of modular neural network architectures for time series prediction. We have a cluster configuration of 16 computers and the application is executed using the Matlab Distributed Computing Engine included in MATLAB r2006b. The Linux Fedora Core VI Operating System was installed and configured for the cluster execution due to its high performance, scalability and because it presents innumerable benefits that facilitate the implementation of distributed computing applications. The first part of this paper presents the theoretical framework with basic concepts like times series, artificial neural networks, genetic algorithms, and parallel genetic algorithms. The second part of this paper presents the procedure for configuring the cluster of computers, requirements, experiences and main problems that were encountered. Also, the development of the project is presented explaining as it was initially proposed and the adjustments that were required. The third part of this paper presents the obtained results for the time series prediction using tables, graphics and describing each one of them. Finally the conclusions and future works are presented.
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By
Morales, Jose; Castillo, Oscar; Soria, Jose
2 Citations
The fuzzy systems present some characteristics that the classical control systems (PI, PD and PID) don’t have, like smoother control, noise immunity, important mathematical complexity reduction, little mathematical knowledge of the model work, and they can obtain results from imprecise data. Broadly stated, fuzzy logic control attempts to come to terms with the informal nature of the control design process. In its most basic form, the socalled Mamdani architecture is directly translating external performance specifications and observations of plant behavior into a rulebased linguistic control strategy. This architecture forms the backbone of the great majority of fuzzy logic control systems reported in the literature in the past years. This paper is based on the fuzzy Lyapunov synthesis, to determine the systems stability, which is based on the Lyapunov criterion; this concept was introduced by Margaliot to adjust the Lyapunov criteria by considering linguistic variables instead of numeric variables to determine the systems stability. The stability will be proving on Mamdani’s architecture fuzzy logic systems type1 and type2 respectively.
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By
Maldonado, Yazmín; Montiel, Oscar; Sepúlveda, Roberto; Castillo, Oscar
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11 Citations
In the last years, several algorithms to implement the fuzzification stage for Very Large Scale of Integration (VLSI) Integrated Circuits (IC) using a Hardware Description Language (HDL) have been developed. In this work it is presented a proposal based in the arithmetic calculation of the slopes in triangular and trapezoidal membership functions to obtain a fuzzified value. We used an arithmetic calculation algorithm to implement trapezoidal and triangular membership functions. This proposal is different to others that at present time are currently used. We discuss the advantages and disadvantages of this implementation. A methodology to test and validate this stage through the Xilinx System Generator is described.
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By
Castillo, Oscar; Ochoa, Patricia; Soria, José
4 Citations
The proposal described in this paper uses the Differential Evolution (DE) algorithm as an optimization method in which we want to dynamically adapt its parameters using fuzzy logic control systems, with the goal that the fuzzy system gives the optimal parameter of the DE algorithm to find better results, depending on the type of problems the DE is applied.
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