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YáñezMárquez, Cornelio; LópezYáñez, Itzamá; AldapePérez, Mario; CamachoNieto, Oscar; ArgüellesCruz, Amadeo José; VilluendasRey, Yenny
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The current paper contains the theoretical foundation for the offthemainstream model known as AlphaBeta associative memories (
$$\alpha \beta $$
model). This is an unconventional computation model designed to operate as an associative memory, whose main application is the solution of pattern recognition tasks, particularly for pattern recall and pattern classification. Although this model was devised, proposed and created in 2002, it is worth noting that its theoretical support remains unpublished to this day. This is despite the fact that more than a hundred scientific articles have been published with applications, improvements, and new models derived from the
$$\alpha \beta $$
model. The present paper includes all the required definitions, and the rigorous mathematical demonstrations of the lemmas and theorems, explaining the operation of the
$$\alpha \beta $$
model, as well as the original models it has inspired or that have been derived from it. Also, brief descriptions of 60 selected articles related to the
$$\alpha \beta $$
model are presented. These latter works illustrate the competitiveness (and sometimes superiority) of several extensions and models derived from the original
$$\alpha \beta $$
model, when compared against some models and paradigms present in the mainstream current scientific literature.
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YáñezMárquez, Cornelio; SánchezFernández, Luis P.; LópezYáñez, Itzamá
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3 Citations
In this paper, we show how the binary AlphaBeta associative memories, created and developed by YáñezMárquez, and introduced in [13], can be used to operate with gray level patterns (namely graylevel images), improving the results presented by Sossa et. al. in [4]. To achieve our goal, given a fundamental set of graylevel patterns, we find the binary representation of each entry, then we build a binary AlphaBeta associative memory. After that, a given gray level pattern or a distorted version of it is recalled by converting its entries to a binary representation, then recalling it with the binary associative memory, and finally converting again this binary output pattern into a gray level pattern. Experimental results show the efficiency of the new memories. It is important to point out that this solution is more simple and elegant than that of the presented in [4].
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AldapePérez, Mario; YáñezMárquez, Cornelio; ArgüellesCruz, Amadeo José
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Associative memories have a number of properties, including a rapid, compute efficient bestmatch and intrinsic noise tolerance that make them ideal for many applications. However, a significant bottleneck to the use of associative memories in realtime systems is the amount of data that requires processing. Notwithstanding, AlphaBeta Associative Memories have been widely used for color matching in industrial processes [1], text translation [2] and image retrieval applications [3]. The aim of this paper is to present the work that produced a dedicated hardware design, implemented on a field programmable gate array (FPGA) that applies the AlphaBeta Associative Memories model for fingerprint verification tasks. Along the experimental phase, performance of the proposed associative memory architecture is measured by learning large sequences of symbols and recalling them successfully. As a result, a simple but efficient embedded processing architecture that overcomes various challenges involved in pattern recognition tasks is implemented on a Xilinx Spartan3 FPGA.
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SánchezFernández, Luis P.; YáñezMárquez, Cornelio; Pogrebnyak, Oleksiy
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This industrial processes monitoring based on a neural network presents low runtime, and it useful for critical time tasks with periodic processing. This method allows the time prediction in which a variable will arrive to abnormal or important values. The data of each variable are used to estimate the parameters of a continuous mathematical model. At this moment, four models are used: firstorder or secondorder in three types (critically damped, overdamped or underdamped). An optimization algorithm is used for estimating the model parameters for a dynamic response to step input function, because this is the most frequent disturbance. Before performing the estimation, the most appropriate model is determined by means of a feedforward neural network.
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RománGodínez, Israel; YáñezMárquez, Cornelio
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1 Citations
Most heteroassociative memories models intend to achieve the recall of the entire trained pattern. The AlphaBeta associative memories only ensure the correct recall of the trained patterns in autoassociative memories, but not for the heteroassociative memories. In this work we present a new algorithm based on the AlphaBeta Heteroassociative memories that allows, besides correct recall of some altered patterns, perfect recall of all the trained patterns, without ambiguity. The theoretical support and some experimental results are presented.
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LópezYáñez, Itzamá; Sheremetov, Leonid; YáñezMárquez, Cornelio
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2 Citations
The paper describes a novel associative model for the forecasting of time series in petroleum engineering. The model is based on the Gamma classifier, which is inspired on the AlphaBeta associative memories, taking the alpha and beta operators as basis for the gamma operator. The objective is to reproduce and predict future oil production in different scenarios in an adjustable time window. The distinctive features of the experimental data set are spikes, abrupt changes and frequent discontinuities, which considerably decrease the precision of traditional forecasting methods. As experimental results show, this classifierbased predictor exhibits competitive performance. The advantages and limitations of the model, as well as lines of improvement, are discussed.
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RománGodínez, Israel; LópezYáñez, Itzamá; YáñezMárquez, Cornelio
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1 Citations
The Lernmatrix, which is the first known model of associative memory, is a hetereoassociative memory that presents the problem of incorrect pattern recall, even in the fundamental set, depending on the associations. In this work we propose a new algorithm and the corresponding theoretical support to improve the recalling capacity of the original model.
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MorenoMoreno, Prudenciano; YáñezMárquez, Cornelio
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The current paper is focused on the evolution and future of discussions on the application of Information Technologies on education and pedagogy from a conceptual perspective. The analyzed debate revolves around those who back New Communication and Information Technologies (NCITs) as a technicalfunctional expression of modernity (the Accolatory); and the opposites (the Dismissive), both postmodern —simply critics and skeptics— and antimodern, decidedly contrary to the use of NCITs. Finally, we conclude with a proposal of conciliation of all the above positions into a new educative vision, transmodern or metamodern, based on the advances of the socalled “Integral Theory of the Whole”.
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AcevedoMosqueda, María Elena; YáñezMárquez, Cornelio; LópezYáñez, Itzamá
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2 Citations
Most models of Bidirectional associative memories intend to achieve that all trained pattern correspond to stable states; however, this has not been possible. Also, none of the former models has been able to recall all the trained patterns. In this work we introduce a new model of bidirectional associative memory which is not iterative and has no stability problems. It is based on the AlphaBeta associative memories. This model allows, besides correct recall of noisy patterns, perfect recall of all trained patterns, with no ambiguity and no conditions. An example of fingerprint recognition is presented.
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LopezMartin, Cuauhtemoc; YáñezMárquez, Cornelio; GutierrezTornes, Agustin
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No single software development estimation technique is best for all situations. A careful comparison of the results of several approaches is most likely to produce realistic estimates. On the other hand, unless engineers have the capabilities provided by personal training, they cannot properly support their teams or consistently and reliably produce quality products. In this paper, an investigation aimed to compare a personal Fuzzy Logic System (FLS) with linear regression is presented. The evaluation criteria are based upon ANOVA of MRE and MER, as well as MMRE, MMER and pred(25). One hundred five programs were developed by thirty programmers. From these programs, a FLS is generated for estimating the effort of twenty programs developed by seven programmers. The adequacy checking as well as a validation of the FLS are made. Results show that a FLS can be used as an alternative for estimating the development effort at personal level.
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AcevedoMosqueda, María Elena; YáñezMárquez, Cornelio; LópezYáñez, Itzamá
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2 Citations
Most models of Bidirectional Associative Memories intend to achieve that all trained patterns correspond to stable states; however, this has not been possible. Also, none of the former models has been able to recall all the trained patterns. A new model which appeared recently, called AlphaBeta Bidirectional Associative Memory (BAM), recalls 100% of the trained patterns, without error. Also, the model is non iterative and has no stability problems. In this work the analysis of time and space complexity of the AlphaBeta BAM is presented.
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AldapePérez, Mario; YáñezMárquez, Cornelio; ArgüellesCruz, Amadeo José
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2 Citations
Performance in most pattern classifiers is improved when redundant or irrelevant features are removed, however, this is mainly achieved by high demanding computational methods or successive classifiers construction. This paper shows how Associative Memories can be used to get a mask value which represents a subset of features that clearly identifies irrelevant or redundant information for classification purposes, therefore, classification accuracy is improved while significant computational costs in the learning phase are reduced. An optimal subset of features allows register size optimization, which contributes not only to significant power savings but to a smaller amount of synthesized logic, furthermore, improved hardware architectures are achieved due to functional units size reduction, as a result, it is possible to implement parallel and cascade schemes for pattern classifiers on the same ASIC.
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Guzmán, Enrique; Pogrebnyak, Oleksiy; Fernández, Luis Sánchez; YáñezMárquez, Cornelio
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2 Citations
One of the most serious problems in vector quantization is the high computational complexity at the encoding phase. This paper presents a new fast search algorithm for vector quantization based on Extended Associative Memories (FSAEAM). In order to obtain the FSAEAM, first, we used the Extended Associative Memories (EAM) to create an EAMcodebook applying the EAM training stage to the codebook produced by the LBG algorithm. The result of this stage is an associative network whose goal is to establish a relation between training set and the codebook generated by the LBG algorithm. This associative network is EAMcodebook which is used by the FSAEAM. The FSAEAM VQ process is performed using the recalling stage of EAM. This process generates a set of the class indices to which each input vector belongs. With respect to the LBG algorithm, the main advantage offered by the proposed algorithm is high processing speed and low demand of resources (system memory), while the encoding quality remains competitive.
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YáñezMárquez, Cornelio; LópezYáñez, Itzamá; Luz Sáenz Morales, Guadalupe
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3 Citations
In later years, different environmental phenomena have attracted the attention of artificial intelligence and machine learning researchers. In particular, several research groups have applied genetic algorithms and artificial neural networks to the analysis of data related to atmospheric and environmental sciences. In the current work, the results of applying the Gamma classifier to the analysis and prediction of air quality data related to the Mexico City Air Quality Metropolitan Index (IMECA in Spanish) are presented.
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LópezLeyva, Luis Octavio; YáñezMárquez, Cornelio; FloresCarapia, Rolando; CamachoNieto, Oscar
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1 Citations
In this paper we present a new model appropriate for pattern recognition tasks. This new model, called αβ Associative Model, arises when taking theoretical elements from the αβ associative memories, and they are merged with several new mathematical transforms. When applied to handwritten digits recognition, namely in the MNIST database, the αβ Associative Model exhibits competitive results against some of the most widely known algorithms currently available in scientific literature.
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FerreiraSantiago, Angel; LópezMartín, Cuauhtémoc; YáñezMárquez, Cornelio
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1 Citations
A qualitative common perception of the software industry is that it finishes its projects late and over budget, whereas from a quantitative point of view, only 39 % of software projects are finished on time compared to the schedule when the project started. This low percentage has been attributed to factors such as unrealistic time frames and lack of planning regarding poor prediction. The main techniques used for predicting project schedule have mainly been based on expert judgment and mathematical models. In this study, a new model, derived from the multivariate adaptive regression splines (MARS) model, is proposed. This new model, optimized MARS (OMARS), uses a simulated annealing process to find a transformation of the input data space prior to applying MARS in order to improve accuracy when predicting the schedule of software projects. The prediction accuracy of the OMARS model is compared to that of standalone MARS and a multiple linear regression (MLR) model with a logarithmic transformation. The two independent variables used for training and testing the models are functional size, which corresponds to a composite value of 19 independent variables, and the maximum size of the team of developers. The data set of projects was obtained from the International Software Benchmarking Standards Group (ISBSG) Release 11. Results based on the absolute residuals and t paired and Wilcoxon statistical tests showed that prediction accuracy with OMARS is statistically better than that with the MARS and MLR models.
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AcevedoMosqueda, María Elena; YáñezMárquez, Cornelio; LópezYáñez, Itzamá
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14 Citations
In this work a new Bidirectional Associative Memory model, surpassing every other past and current model, is presented. This new model is based on Alpha–Beta associative memories, from whom it inherits its name. The main and most important characteristic of Alpha–Beta bidirectional associative memories is that they exhibit perfect recall of all patterns in the fundamental set, without requiring the fulfillment of any condition. The capacity they show is 2^{min(n,m)}, being n and m the input and output patterns dimensions, respectively. Design and functioning of this model are mathematically founded, thus demonstrating that pattern recall is always perfect, with no regard to the trained pattern characteristics, such as linear independency, orthogonality, or Hamming distance. Two applications illustrating the optimal functioning of the model are shown: a translator and a fingerprint identifier.
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