Learning convergence analysis for Takagi-Sugeno Fuzzy Neural Networks

Author

Chiang, Tung-sheng ; Liu, Peter ; Yang, Chang-En

Author_Institution

Dept. of Electr. Eng., Ching-Yun Univ., Jungli, Taiwan

fYear

2012

fDate

10-15 June 2012

Firstpage

1

Lastpage

6

Abstract

In this paper, we provide a mathematical formulation of the Takagi-Sugeno Fuzzy Neural Network (TS-FNN) to study convergence properties. Note that we describe both information retrieval and learning rules by algebraic equations in matrix form. We then investigate the convergence characteristics and learning behaviors for the TS-FNN by use of these algebraic equations and the eigenvalues of derived matrices. Numerical examples are carried out to further verify the analysis.

Keywords

convergence of numerical methods; eigenvalues and eigenfunctions; fuzzy control; learning systems; matrix algebra; neurocontrollers; TS-FNN; Takagi-Sugeno fuzzy neural networks; algebraic equations; convergence characteristics; convergence properties; information retrieval; learning behaviors; learning convergence analysis; learning rules; matrices eigenvalues; matrix form; Convergence; Eigenvalues and eigenfunctions; Equations; Fuzzy control; Fuzzy neural networks; Mathematical model; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on

Conference_Location

Brisbane, QLD

ISSN

1098-7584

Print_ISBN

978-1-4673-1507-4

Electronic_ISBN

1098-7584

Type

conf

DOI

10.1109/FUZZ-IEEE.2012.6251318

Filename

6251318