Please use this identifier to cite or link to this item:
Title: A dynamic weight-fuzzy neural network for nonlinear dynamic system control
Authors: Kwok, DP
Li, CK
Leung, TP
Deng, ZD
Sun, ZQ
Keywords: Adaptive control
Control system synthesis
Fuzzy control
Fuzzy neural nets
Learning (artificial intelligence)
Neural net architecture
Nonlinear control systems
Nonlinear dynamical systems
Robust control
Issue Date: 2001
Publisher: IEEE
Source: The 10th IEEE International Conference on Fuzzy Systems, 2001, 2-5 December 2001, v. 3, p. 852-855 How to cite?
Abstract: A class of fuzzy neural network with dynamic weights is proposed and its corresponding network topological architecture with suitable supervised learning algorithm is presented. Using the proposed network in control system design, a priori knowledge of the control system is not essential and this includes the order of the control system. The proposed network is applied to the control of a highly nonlinear pH-neutralization process. Simulation shows that the proposed dynamic learning control strategy has better dynamic quality, stronger robustness, adaptability and intelligence while comparing to the conventional control techniques, which demand the explicit and quantitative mathematical model of the system under control.
ISBN: 0-7803-7293-X
DOI: 10.1109/FUZZ.2001.1009089
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

Last Week
Last month
Citations as of Aug 14, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.