標題: Cycle-symmetric matrices and convergent neural networks
作者: Shih, CW
Weng, CW
應用數學系
Department of Applied Mathematics
關鍵字: neural networks;cycle-symmetric matrix;Lyapunov function;convergence of dynamics
公開日期: 15-十一月-2000
摘要: This work investigates a class of neural networks with cycle-symmetric connection strength. We shall show that, by changing the coordinates, the convergence of dynamics by Fiedler and Gedeon [Physica D 111 (1998) 288] is equivalent to the classical results. This presentation also addresses the extension of the convergence theorem to other classes of signal functions with saturations. In particular, the result of Cohen and Grossberg [IEEE Trans. Syst. Man Cybernet. SMC-13 (1983) 815] is recast and extended with a more concise verification. (C) 2000 Elsevier Science B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/S0167-2789(00)00134-2
http://hdl.handle.net/11536/30134
ISSN: 0167-2789
DOI: 10.1016/S0167-2789(00)00134-2
期刊: PHYSICA D
Volume: 146
Issue: 1-4
起始頁: 213
結束頁: 220
顯示於類別:期刊論文


文件中的檔案:

  1. 000165117000008.pdf