Tuning of a TS fuzzy output regulator using the steepest descent approach and ANFIS
type: Item
instance of: scholarly article
title: Tuning of a TS fuzzy output regulator using the steepest descent approach and ANFIS
zbMATH Open document ID: 1299.93203
DOI: 10.1155/2013/873430
author: Ricardo Tapia-Herrera
author: J. A. Meda-Campaña
author: Samuel Alcántara-Montes
author: Tonatiuh Hernández-Cortés
author: Lizbeth Salgado-Conrado
published in: Mathematical Problems in Engineering
publication date: 2014-11-24T00:00:00Z
review text: Summary: The exact output regulation problem for Takagi-Sugeno (TS) fuzzy models, designed from linear local subsystems, may have a solution if input matrices are the same for every local linear subsystem. Unfortunately, such a condition is difficult to accomplish in general. Therefore, in this work, an adaptive network-based fuzzy inference system (ANFIS) is integrated into the fuzzy controller in order to obtain the optimal fuzzy membership functions yielding adequate combination of the local regulators such that the output regulation error in steady-state is reduced, avoiding in this way the aforementioned condition. In comparison with the steepest descent method employed for tuning fuzzy controllers, ANFIS approximates the mappings between local regulators with membership functions which are not necessary known functions as Gaussian bell (gbell), sigmoidal, and triangular membership functions. Due to the structure of the fuzzy controller, Levenberg-Marquardt method is employed during the training of ANFIS.
Mathematics Subject Classification ID: 93C95
Mathematics Subject Classification ID: 93C42
zbMATH DE Number: 6372710
Wikidata QID: Q59030866
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/873430
OpenAlex ID: W2017851137
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description: scientific article; zbMATH DE number 6372710