Practical stability issues in CMAC neural network control systems

Fu-Chuang Chen; Chih Horng Chang

doi:10.1109/ACC.1994.735109

Practical stability issues in CMAC neural network control systems

Fu-Chuang Chen^*, Chih Horng Chang

^*Corresponding author for this work

Department of Electrical and Computer Engineering

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

The CMAC neural network is a practical tool for improving existing nonlinear control systems. Although many researchers report that it have good performance, our results suggest that the CMAC control system can go unstable. The unstable phenomena are quantitatively studied, and some methods for improving stability are suggested.

Original language	English
Article number	735109
Pages (from-to)	2945-2946
Number of pages	2
Journal	Proceedings of the American Control Conference
Volume	3
DOIs	https://doi.org/10.1109/ACC.1994.735109
State	Published - 29 Jun 1994
Event	Proceedings of the 1994 American Control Conference. Part 1 (of 3) - Baltimore, MD, USA Duration: 29 Jun 1994 → 1 Jul 1994

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title = "Practical stability issues in CMAC neural network control systems",

abstract = "The CMAC neural network is a practical tool for improving existing nonlinear control systems. Although many researchers report that it have good performance, our results suggest that the CMAC control system can go unstable. The unstable phenomena are quantitatively studied, and some methods for improving stability are suggested.",

author = "Fu-Chuang Chen and Chang, {Chih Horng}",

year = "1994",

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AU - Chang, Chih Horng

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N2 - The CMAC neural network is a practical tool for improving existing nonlinear control systems. Although many researchers report that it have good performance, our results suggest that the CMAC control system can go unstable. The unstable phenomena are quantitatively studied, and some methods for improving stability are suggested.

AB - The CMAC neural network is a practical tool for improving existing nonlinear control systems. Although many researchers report that it have good performance, our results suggest that the CMAC control system can go unstable. The unstable phenomena are quantitatively studied, and some methods for improving stability are suggested.

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U2 - 10.1109/ACC.1994.735109

DO - 10.1109/ACC.1994.735109

M3 - Conference article

AN - SCOPUS:0028604150

SN - 0743-1619

VL - 3

SP - 2945

EP - 2946

JO - Proceedings of the American Control Conference

JF - Proceedings of the American Control Conference

M1 - 735109

T2 - Proceedings of the 1994 American Control Conference. Part 1 (of 3)

Y2 - 29 June 1994 through 1 July 1994

ER -

Practical stability issues in CMAC neural network control systems

Abstract

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