Title :
Input-to-state stability (ISS) analysis for dynamic neural networks
Author :
Sanchez, Edgar N. ; Perez, Jose P.
Author_Institution :
Sch. of Phys. & Math. Sci., Univ. Autonoma de Nuevo Leon, Mexico
fDate :
11/1/1999 12:00:00 AM
Abstract :
In this paper a novel approach to assess the stability of dynamic neural networks is presented. Using a Lyapunov function, we determine conditions to guarantee input-to-state stability (ISS) which also ensures global asymptotic stability (GAS). The applicability of these conditions is illustrated by two examples
Keywords :
Lyapunov methods; asymptotic stability; neural nets; stability; Lyapunov function; dynamic neural networks; global asymptotic stability; input-to-state stability analysis; Associative memory; Asymptotic stability; Automatic control; Lyapunov method; Mathematical model; Neural networks; Nonlinear systems; Pattern recognition; Physics; Stability analysis;
Journal_Title :
Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on
