DocumentCode
523649
Title
Nonlinear System Modeling Based on IFCNN
Author
Xia, Liu ; Yubo, Duan ; Xiuju, Yang
Author_Institution
Dept. of Electr. & Inf. Eng., Daqing Pet. Inst., Daqing, China
Volume
2
fYear
2010
fDate
11-12 May 2010
Firstpage
813
Lastpage
816
Abstract
This paper for the shortcomings of conventional BP algorithm which has slow convergence and falls into local minimum easily, the nonlinear self-feedback term is introduced into this algorithm. Thus chaotic BP algorithm (CBPA) is given. The weight of fuzzy neural network (FNN) is trained and learned by using it. Thus an introduction-type fuzzy chaotic neural network (IFCNN) is constituted. Then simulation of nonlinear system based on IFCNN given is proposed. Simulation results show that the designed IFCNN has the same and complex dynamic characteristics with chaotic system, which has good modeling capabilities for nonlinear system. And with the chaotic BP algorithm training parameters, it has fast convergence, mixed search capability, being able to be out of local minimum.
Keywords
backpropagation; chaos; feedback; fuzzy neural nets; modelling; nonlinear control systems; IFCNN; backpropagation; chaotic BP algorithm; complex dynamic characteristics; conventional BP algorithm; fuzzy chaotic neural network; mixed search capability; nonlinear system modeling; Approximation algorithms; Automation; Chaos; Convergence; Fuzzy neural networks; Interference; Modeling; Neural networks; Nonlinear systems; Petroleum; Chaotic BP Algorithm; Fuzzy Neural Network; Nonlinear System Modeling;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on
Conference_Location
Changsha
Print_ISBN
978-1-4244-7279-6
Electronic_ISBN
978-1-4244-7280-2
Type
conf
DOI
10.1109/ICICTA.2010.421
Filename
5522759
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