Systems identification using type-2 fuzzy neural network (type-2 FNN) systems

Author

Lee, Ching-Hung ; Lin, Yu-Ching ; Lai, Wei-Yu

Author_Institution

Dept. of Electr. Eng., Yuan-Ze Univ., Chung-li, Taiwan

Volume

3

fYear

2003

fDate

16-20 July 2003

Firstpage

1264

Abstract

This paper presents a type-2 fuzzy neural network system (type-2 FNN) and its learning algorithm using back-propagation algorithm. In our previous results, the FNN system using type-1 fuzzy logic systems (FLS) is called type-1 FNN system. It has the properties of parallel computation scheme, easy to implement, fuzzy logic inference system, and parameters convergence. For considering the fuzzy rules uncertainties, we use the type-2 FLSs to develop a type-2 FNN system. The type-2 fuzzy sets let us model and minimize the effects of uncertainties in rule-based fuzzy logic systems (FLSs). In this paper, the previous results of type-1 FNN are extended to a type-2 one. In addition, the corresponding learning algorithm is derived by back-program algorithm. Several examples are presented to illustrate the effectiveness of our approach.

Keywords

backpropagation; fuzzy logic; fuzzy neural nets; fuzzy set theory; fuzzy systems; identification; inference mechanisms; nonlinear systems; back-propagation algorithm; fuzzy logic inference system; intelligent systems; learning algorithm; neuro-fuzzy systems; parallel computation scheme; parameters convergence; systems identification; type-1 fuzzy logic systems; type-2 fuzzy neural network systems; Convergence; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Neural networks; Signal processing algorithms; System identification; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence in Robotics and Automation, 2003. Proceedings. 2003 IEEE International Symposium on

Print_ISBN

0-7803-7866-0

Type

conf

DOI

10.1109/CIRA.2003.1222178

Filename

1222178