標題: Complete stability for a class of cellular neural networks
作者: Shih, CW
應用數學系
Department of Applied Mathematics
公開日期: 1-一月-2001
摘要: This work investigates a class of lattice dynamical systems originated from cellular neural networks. In the vector field of this class, each component of the state vector and the output vector is related through a sigmoidal nonlinear output function. For two types of sigmoidal output functions, Liapunov functions have been constructed in the literatures. Complete stability has been studied for these systems using LaSalle's invariant principle on the Liapunov functions. The purpose of this presentation is two folds. The first one is to construct Liapunov functions for more general sigmoidal output functions. The second is to extend the interaction parameters into a more general class, using an approach by Fiedler and Gedeon. This presentation also emphasizes the complete stability when the equilibrium is not isolated for the standard cellular neural networks.
URI: http://dx.doi.org/10.1142/S0218127401002055
http://hdl.handle.net/11536/29990
ISSN: 0218-1274
DOI: 10.1142/S0218127401002055
期刊: INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS
Volume: 11
Issue: 1
起始頁: 169
結束頁: 177
顯示於類別:期刊論文


文件中的檔案:

  1. 000167608400013.pdf