Fuzzy neural network model for a class of nonlinear systems

Author

Wang, Zhenlei ; Cao, Guangyi ; Zhu, Xinjian

Author_Institution

Inst. of Fuel Cell, Shanghai Jiao Tong Univ., China

Volume

1

fYear

2003

fDate

14-17 Dec. 2003

Firstpage

91

Abstract

For a class of nonlinear systems that the entire operation region can be divided into several operating regions and the property of each subsystem is linear, a modified fuzzy neural network (FNN) is designed as identifier. The prior knowledge of the system is considered adequately in the FNN model. The local model network of FNN is used to approximate the subsystem of each zone according to ARMAX model. The output of FNN model is the interpolation of local model network outputs with the output of the fuzzy decision-making layer. A new cost function is used when training FNN model. It can reduce over-fit of FNN model.

Keywords

decision making; fuzzy neural nets; identification; learning (artificial intelligence); neurocontrollers; nonlinear control systems; fuzzy decision-making layer; fuzzy neural network; identification; nonlinear systems; operating regions; Chemicals; Control systems; Cost function; Decision making; Fuel cells; Fuzzy control; Fuzzy neural networks; Interpolation; Nonlinear systems; Power system modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on

Conference_Location

Nanjing

Print_ISBN

0-7803-7702-8

Type

conf

DOI

10.1109/ICNNSP.2003.1279220

Filename

1279220