NDLI: Robust identification for unknown nonlinear multivariable systems based on dynamic neural networks

Content Provider	IEEE Xplore Digital Library
Author	Qiong-hai Dai Zhang Tao Yu-mei Zhang Tian-you Chai Li-hua Xia
Copyright Year	1996
Description	Author affiliation: Res. Centre of Autom., Northeastern Univ., Shenyang, China (Qiong-hai Dai)
Abstract	In this paper a learning and identification scheme for a class of unknown multivariable nonlinear system using dynamic neural networks (DNN) is presented. A DNN identifier is employed to perform "black box" identification. The identification scheme, based on DNN model, is then developed using Lyapunov synthesis approach with the projection modification method. The feature of this approach is that neither off-line learning phase nor all plant states for measurement are required. It is shown theoretically that the identified system is robust stable and the identified error is ensured in a stable region with respect to modeling errors and unmodeled dynamics. Simulation results with unknown nonlinearities are given to demonstrate the effectiveness of the proposed identification algorithm.
Starting Page	2244
Ending Page	2249
File Size	416804
Page Count	6
File Format	PDF
ISBN	0780332105
DOI	10.1109/ICNN.1996.549250
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	Robustness MIMO Neural networks Nonlinear dynamical systems Nonlinear systems Robust stability Algorithm design and analysis Stability analysis Erbium Automation
Content Type	Text
Resource Type	Article

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