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Stability of analogue neural classification networks

 

作者: E. NOLDUS,   R. VINGERHOEDS,   M. LOCCUFIER,  

 

期刊: International Journal of Systems Science  (Taylor Available online 1994)
卷期: Volume 25, issue 1  

页码: 19-31

 

ISSN:0020-7721

 

年代: 1994

 

DOI:10.1080/00207729408928942

 

出版商: Taylor & Francis Group

 

数据来源: Taylor

 

摘要:

A general frequency domain stability criterion is presented for autonomous nonlinear dynamical systems that possess several non-linearities and several equilibrium states. As special cases, the result is shown to contain well-known criteria for non-oscillatory behaviour of non-linear feedback loops, of non-linear electrical RLC-circuits and analogue neural networks. The proof relies on a single Liapunov function which can subsequently be used lo compute regions of attraction for each of the equilibrium states. Therefore the result is particularly useful for the analysis and the design of systems such as neural classification networks, which possess many equilibrium states.

 

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