ART-Based Model Set for Pattern Recognition: FasArt Family

Sainz-Palmero, G.I.; Dimitriadis, Yannis; Izquierdo, José María; Sánchez, Eduardo Gómez

doi:10.1142/9789812792204_0007

Cited by 17 publications

(14 citation statements)

References 18 publications

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“…One well-known OT has been involved in this work: P-QR. The FRBSs are generated by a neuro-fuzzy system, FasArt [11], [12], which is a scatter fuzzy system. Now, in order to generate a better rule selection, this is carried out following the guidance of different points of view concerned with:…”

Section: Trade-offmentioning

confidence: 99%

Selection of rules by orthogonal transformations and genetic algorithms to improve the interpretability in fuzzy rule based systems

Rey¹,

Galende²,

Sainz

et al. 2013

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Fuzzy modeling is one of the best known techniques to model systems and processes. In most cases, as in data-driven fuzzy modeling, these fuzzy models reach a high accuracy, but show poor performance in complexity or interpretability, which are key aspects of Fuzzy Logic.There are several approaches in the literature to deal with the complexity and interpretability challenges for fuzzy rule based systems (FRBSs). In this paper, a post-processing approach is proposed via a genetic rule selection based on the relevance of each rule (using Orthogonal Transformations (OTs), in this case P-QR) and the well-known accuracy-interpretability trade-off. The main objective is to check the true significance, drawbacks and advantages of the rule selection based on OTs to manage the accuracy-interpretability trade-off.In order to achieve this aim, a neuro-fuzzy system (FasArtFuzzy Adaptive System ART based) and several case studies from the KEEL Project Repository are used to tune and check this selection of rules based on rule relevance by OTs, genetic selection and accuracy-interpretability trade-off. This neuro-fuzzy system generates Mamdani FRBSs, in an approximate way. SPEA2 is the multi-objective evolutionary algorithm (MOEA) tool used to tune the proposed rule selection, and different interpretability measures have been considered.

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Section: Trade-offmentioning

confidence: 99%

Selection of rules by orthogonal transformations and genetic algorithms to improve the interpretability in fuzzy rule based systems

Rey¹,

Galende²,

Sainz

et al. 2013

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…The FasArt model (Cano Izquierdo et al 2001;Sainz Palmero et al 2000) is a neuro fuzzy system based on the adaptive resonance theory (ART). FasArt introduces an equivalence between the activation function of each FasArt neuron and a membership function.…”

Section: Neuro-fuzzy System Fasartmentioning

confidence: 99%

“…In this way, FasArt is equivalent to a Mamdani-type FRBSs with: Fuzzification by single point, Inference by product, and Defuzzification by average of fuzzy set centers. A full description of this model can be found in Cano Izquierdo et al (2001) and Sainz Palmero et al (2000).…”

Section: Neuro-fuzzy System Fasartmentioning

confidence: 99%

“…This aim is carried out through a biobjective (accuracy and interpretability) genetic approach via rule selection, and a simple and understandable set of indices concerning accuracy-interpretability. This proposal takes advantage of two well-known fuzzy modeling algorithms, Fuzzy Adaptive System ART based (FasArt) (Cano Izquierdo et al 2001;Sainz Palmero et al 2000) and Neuro-Fuzzy Function Approximation (NefProx) (Nauck and Kruse 1999), which are very popular in technical and engineering domains, improving their fuzzy performance through the reduction of their complexity in order to achieve a better accuracy-interpretability trade-off using MOEAs. In this way, the fuzzy rules describing the behavior of the modeled problem are accessible in a more interpretable way, and thus extra knowledge and performance from these fuzzy models are obtained.…”

Section: Introductionmentioning

confidence: 99%

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Complexity reduction and interpretability improvement for fuzzy rule systems based on simple interpretability measures and indices by bi-objective evolutionary rule selection

2011

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The aim of this paper is to develop a general post-processing methodology to reduce the complexity of data-driven linguistic fuzzy models, in order to reach simpler fuzzy models preserving enough accuracy and better fuzzy linguistic performance with respect to their initial values. This post-processing approach is based on rule selection via the formulation of a bi-objective problem with one objective focusing on accuracy and the other on interpretability. The latter is defined via the aggregation of several interpretability measures, based on the concepts of similarity and complexity of fuzzy systems and rules. In this way, a measure of the fuzzy model interpretability is given. Two neuro-fuzzy systems for providing initial fuzzy models, Fuzzy Adaptive System ART based and NeuroFuzzy Function Approximation and several case studies, data sets from KEEL Project Repository, are used to check this approach. Both fuzzy and neuro-fuzzy systems generate Mamdani-type fuzzy rule-based systems, each with its own particularities and complexities from the point of view of the fuzzy sets and the rule generation. Based on these systems and data sets, several fuzzy models are generated to check the performance of the proposal under different restrictions of complexity and fuzziness.

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“…-Feedforward network (Haykin, 1999), that is the well-known multilayer perceptron training with backpropagation algotrithm (Castro et al, 2002). Cano Izquierdo et al, 2001;Sainz Palmero et al, 2000) and FasBack (Cano Izquierdo et al, 2001), that can be considered similar to a fuzzy logic system with fuzzyfication by simple point, inference by product and defuzzification by average of fuzzy set centers.…”

Section: Introductionmentioning

confidence: 99%

Fault Fuzzy Rule Extraction from AC Motors by Neuro-fuzzy Models

Sainz

García

Fuente

2005

Computational Intelligence and Bioinspired Systems

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Abstract. In this paper the knowledge extraction from neural and fuzzy models, and the quality and the explanation capacities of this knowledge are tracked. Nowadays the application of algorithms and methodologies based on artificial neural networks and fuzzy logic are very usual in most of the scientific and technical areas in order to generate models driven by data. But sometimes to obtain a good model by these techniques is not enough when some explanations about the model behaviour are mandatory, and these models are very near, most of the cases, to "black boxes" or its explanatory capacity is very poor. In literature, several methods are been published in order to extraction, simplification and interpretability of the knowledge stored in these types of models.In this paper a real problem is involved: to model an AC motor on several functioning modes (faults) by several neural/fuzzy approaches, making a comparison on the knowledge extracted from each one: Feedforward network + Backpropagation, Substractive Clustering + ANFIS, FasArt and FasBack.

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ART-Based Model Set for Pattern Recognition: FasArt Family

Cited by 17 publications

References 18 publications

Selection of rules by orthogonal transformations and genetic algorithms to improve the interpretability in fuzzy rule based systems

Selection of rules by orthogonal transformations and genetic algorithms to improve the interpretability in fuzzy rule based systems

Complexity reduction and interpretability improvement for fuzzy rule systems based on simple interpretability measures and indices by bi-objective evolutionary rule selection

Fault Fuzzy Rule Extraction from AC Motors by Neuro-fuzzy Models

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