Title :
Input-Output Stability for Differential Neural Networks
Author :
Moran, E.G. ; Labastida, Daishi A Murano
Author_Institution :
Dept. de Mec. y Mecatronica, Inst. Tecnol. y de Estudios Super. de Monterrey, Atizapan de Zaragoza, Mexico
Abstract :
This paper deals with the problem to obtain input-output stability for a certain class of differential neural networks. Hence, by using a Lyapunov function, the conditions to guarantee finite-gain L-stability, which also ensures global exponential stability (GES), are established. Finally, the simulation of a numerical example illustrates the applicability of this approach.
Keywords :
Lyapunov methods; asymptotic stability; neural nets; numerical analysis; Lyapunov function; differential neural networks; finite-gain L-stability; global exponential stability; input-output stability; Asymptotic stability; Equations; Lyapunov methods; Mathematical model; Neural networks; Numerical stability; Stability analysis;
Conference_Titel :
Electronics, Robotics and Automotive Mechanics Conference (CERMA), 2011 IEEE
Conference_Location :
Cuernavaca, Morelos
Print_ISBN :
978-1-4577-1879-3
DOI :
10.1109/CERMA.2011.16
