Open AccessA new criterion of asymptotic stability for Hopfield neural networks with time-varying delay
Author(s) -
Weiru Guo,
Fang Liu
Publication year - 2022
Publication title -
vestnik irkutskogo gosudarstvennogo tehničeskogo universiteta
Language(s) - English
Resource type - Journals
eISSN - 2500-1590
pISSN - 1814-3520
DOI - 10.21285/1814-3520-2021-6-753-761
Subject(s) - hopfield network , artificial neural network , control theory (sociology) , stability criterion , stability (learning theory) , mathematics , computer science , exponential stability , linear matrix inequality , stability conditions , lyapunov function , mathematical optimization , discrete time and continuous time , artificial intelligence , nonlinear system , physics , control (management) , quantum mechanics , machine learning , statistics
The objective of this paper is to analyze the stability of Hopfield neural networks with time-varying delay. For the system to operate in a steady state, it is important to guarantee the stability of Hopfield neural networks with time-varying delay. The Lyapunov-Krasovsky functional method is the main method for investigating the stability of time-delayed systems. On the basis of this method, the stability of Hopfield neural networks with time-varying delay is ana-lysed. It is known that due to such factors as communication time, limited switching speed of various active devices, time delays often arise in various technical systems, which significantly degrade the performance of the system, which can in turn lead to a complete loss of stability. In this regard, a Lyapunov-Krasovsky type delay-product functional was con-structed in the paper, which allows more information about the time delay and reduces the conservatism of the method. Then a generalized integral inequality based on the free matrix was used. A new criterion for asymptotic stability of Hop-field neural networks with time-varying delay, which has less conservatism, was formulated. The effectiveness of the proposed method is illustrated. Thus an asymptotic stability criterion for Hopfield neural networks with time-varying delay was formulated and justified. The expanded Lyapunov-Krasovsky functional is constructed on the basis of delay and quadratic multiplicative functional, and the derivative of the functional is defined by a matrix integral inequality with free weights. The effectiveness of the method is illustrated by a model example.