A fuzzy C‐regression model algorithm using a new PSO algorithm

Summary In this paper, a new methodology is introduced for the identification of the parameters of the multiple‐input–multiple‐output local linear Takagi‐Sugeno fuzzy models using the weighted recursive least squares (WRLS). The WRLS is sensitive to initialization, which leads to no convergence. In...

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Veröffentlicht in:	International journal of adaptive control and signal processing Jg. 32; H. 1; S. 115 - 133
Hauptverfasser:	Taieb, Adel, Soltani, Moez, Chaari, Abdelkader
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Bognor Regis Wiley Subscription Services, Inc 01.01.2018
Schlagworte:	Algorithms chaos adaptive particle swarm optimization Clustering fuzzy C‐regression model clustering algorithm identification multiple‐input multiple‐output Nonlinear systems Parameter identification Particle swarm optimization Regression models Takagi‐Sugeno fuzzy models
ISSN:	0890-6327, 1099-1115
Online-Zugang:	Volltext
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