Fuzzy control for nonlinear systems modeled via neural-network

Zhen Yuan Chen, Cheng Wu Chen, Wei Ling Chiang, Jiing Don Huang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

It is known that backpropagation network is always used to modeling. This study is concerned with the stability problem of a neural network (NN) system which consists of a few subsystems represented by NN models. In this paper, the dynamics of each NN model is converted into linear inclusion representation. Subsequently, based on the representations, the stability conditions in terms of Lyapunov's direct method is derived to guarantee the stability of nonlinear systems. Finally, a numerical example with simulations is given to illustrate the results.

Original languageEnglish
Title of host publicationIEEE ICIT 2002 - 2002 IEEE International Conference on Industrial Technology
Subtitle of host publication"Productivity Reincarnation Through Robotics and Automation"
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages66-71
Number of pages6
ISBN (Electronic)0780376579
DOIs
StatePublished - 2002
EventIEEE International Conference on Industrial Technology, IEEE ICIT 2002 - Bangkok, Thailand
Duration: 11 Dec 200214 Dec 2002

Publication series

NameProceedings of the IEEE International Conference on Industrial Technology
Volume1

Conference

ConferenceIEEE International Conference on Industrial Technology, IEEE ICIT 2002
Country/TerritoryThailand
CityBangkok
Period11/12/0214/12/02

Fingerprint

Dive into the research topics of 'Fuzzy control for nonlinear systems modeled via neural-network'. Together they form a unique fingerprint.

Cite this