Linear systems in a saturated mode and convergence as gain becomes large of asymptotically stable equilibrium points of neural nets

被引:6
作者
Calvert, BD [1 ]
机构
[1] Univ Auckland, Dept Math, Auckland, New Zealand
关键词
D O I
10.1007/BF01225697
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
We study solutions of the "linear system in a saturated mode" (M) ' epsilon Tx + c - partial derivative I(Dn)x. We show that a trajectory is in a constant face of the cube D-n on some interval (0, d]. We answer a question about comparing the two systems: (M) and (H) Cu' = Tv + c - R(-1)u, v = G(lambda u). As lambda --> infinity, limits of v corresponding to asymptotically stable equilibrium points of (H) are asymptotically stable equilibrium points of(M), and the converse is also true. We study the assumptions to see which are required and which may be weakened.
引用
收藏
页码:241 / 267
页数:27
相关论文
共 15 条
[11]   MINIMUM-SEEKING PROPERTIES OF ANALOG NEURAL NETWORKS WITH MULTILINEAR OBJECTIVE FUNCTIONS [J].
VIDYASAGAR, M .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1995, 40 (08) :1359-1375
[12]   LOCATION AND STABILITY OF THE HIGH-GAIN EQUILIBRIA OF NONLINEAR NEURAL NETWORKS [J].
VIDYASAGAR, M .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1993, 4 (04) :660-672
[13]  
VIDYASAGAR M, 1996, INT C AUT ROB CONTR
[14]  
VIDYASAGAR M, 1994, INT C AUT ROB COMP V
[15]   A coupled gradient network approach for static and temporal mixed-integer optimization [J].
Watta, PB ;
Hassoun, MH .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1996, 7 (03) :578-593