NDLI: Fast modelling and control of unknown nonlinear systems via neural networks

Content Provider	IEEE Xplore Digital Library
Author	Hong Wang
Copyright Year	1996
Description	Author affiliation: Dept. of Paper Sci., Univ. of Manchester Inst. of Sci. & Technol., UK (Hong Wang)
Abstract	This paper presents a unified framework for the use of neural networks for the modelling and control of unknown nonlinear systems. The systems are assumed to be expressed by a unknown NARMA model including a set of unknown parameters. At first, it is assumed that the nominal (initial) values of these parameters are known during an initial operation period of the system. By incorporating the nominal parameter set into the structure of neural network, a neural network model for the system can be established via online training of the weights during the initial operation period. Using the trained neural network, the estimation of the parameters is achieved by incorporating the estimated parameters into the neural network model and by constructing a gradient descent based estimation algorithm. The design of the controllers for the unknown nonlinear systems have been discussed and desired results have been obtained via comparing existing neural network based modelling approaches.
Starting Page	2255
Ending Page	2260
File Size	494511
Page Count	6
File Format	PDF
ISBN	0780332105
DOI	10.1109/ICNN.1996.549252
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-06-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nonlinear control systems Nonlinear systems Neural networks Parameter estimation Control systems Robots Power system modeling Error correction Shape control Control system synthesis
Content Type	Text
Resource Type	Article

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