Download Accuracy Improvements in Linguistic Fuzzy Modeling by Jorge Casillas, O. Cordón, Francisco Herrera Triguero, Luis PDF

By Jorge Casillas, O. Cordón, Francisco Herrera Triguero, Luis Magdalena

Fuzzy modeling frequently comes with contradictory standards: interpretability, that's the potential to specific the genuine process habit in a understandable approach, and accuracy, that is the potential to faithfully characterize the genuine process. during this framework, some of the most very important parts is linguistic fuzzy modeling, the place the legibility of the received version is the most goal. This job is generally constructed via linguistic (Mamdani) fuzzy rule-based structures. An energetic study quarter is orientated in the direction of using new ideas and constructions to increase the classical, inflexible linguistic fuzzy modeling with the most goal of accelerating its precision measure. routinely, this accuracy development has been performed with no contemplating the corresponding interpretability loss. at present, new tendencies were proposed attempting to guard the linguistic fuzzy version description energy through the optimization method. Written by means of major specialists within the box, this quantity collects a few consultant researcher that pursue this process.

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Vandewalle. Constructing fuzzy models with linguistic integrity from numerical data-AFRELI algorithm. IEEE Transactions on Fuzzy Systems, 8(5):591-600, 2000. 12. A. Gonzalez and R. perez. A study about the inclusion of linguistic hedges in a fuzzy rule learning algorithm. International Joumal of Uncertainty, Fuzziness and Knowledge-Based Systems, 7(3):257-266, 1999. 13. F. Herrera, M. L. Verdegay. A learning process for fuzzy control rules using genetic algorithms. Fuzzy Sets and Systems, 100: 143-158, 1998.

This search space reduction is performed by two constrains: The maximum number of fuzzy input subspaces, and therefore maximum number of fuzzy rules, is limited by the positive example sets. 1. 2. in Fig. 1), the selection of the subspaces will be more conservative or more generic. 1. divides the input space with a crisp grid bounded by the cross points between labels and, therefore, each example contributes to generate a single rule. 2. divides the input space with a fuzzy grid where each example may contribute to generate sever al rules.

To compensate for this deficiency, genetic algorithms (GAs) have often been used to automate the design of fuzzy systems. Unfortunately, the inconsiderate use of GAs and the temptation to automate every conceivable aspect of the design can lead to the design of fuzzy systems with obscure structures that are not easily interpretable. In this chapter, we describe our efforts for constraining the use of genetic algorithms and evolutionary programming in order to design fuzzy logic controllers (fuzzy systems used in control applications) which are both accurate and interpretable.

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