Identification of Takagi-Sugeno Fuzzy Systems Based on Multi-objective Genetic Algorithms

Identification of Takagi-Sugeno Fuzzy Systems Based on Multi-objective Genetic Algorithms

In this paper we exploit multi-objective genetic algorithms to identify Takagi-Sugeno (TS) fuzzy systems that show simultaneously high accuracy and low complexity. Using this approach, we approximate the Pareto optimal front by first identifying TS models with different structures (i.e., different n...

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Hauptverfasser: Cococcioni, Marco, Guasqui, Pierluigi, Lazzerini, Beatrice, Marcelloni, Francesco
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Applied sciences
Computer science
control theory
systems
Exact sciences and technology
Logical, boolean and switching functions
Theoretical computing
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Lazzerini, Beatrice
Marcelloni, Francesco
description In this paper we exploit multi-objective genetic algorithms to identify Takagi-Sugeno (TS) fuzzy systems that show simultaneously high accuracy and low complexity. Using this approach, we approximate the Pareto optimal front by first identifying TS models with different structures (i.e., different number of rules and input variables), and then performing a local optimization of these models using an ANFIS learning approach. The results obtained allow determining a posteriori the optimal TS system for the specific application. Main features of our approach are selection of the input variables and automatic determination of the number of rules.
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subjects Applied sciences
Computer science
control theory
systems
Exact sciences and technology
Logical, boolean and switching functions
Theoretical computing
title Identification of Takagi-Sugeno Fuzzy Systems Based on Multi-objective Genetic Algorithms
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