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Expanded Lyapunov-Krasovskii Functionals and Stability Analysis in Delayed Neural Networks via Augmented Zero Equality Approach
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Expanded Lyapunov-Krasovskii Functionals and Stability Analysis in Delayed Neural Networks via Augmented Zero Equality Approach
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Journal Article
Expanded Lyapunov-Krasovskii Functionals and Stability Analysis in Delayed Neural Networks via Augmented Zero Equality Approach
2023
Overview
This paper proposes improved Lyapunov-Krasovskii functionals (LKFs) for asymptotic stability of generalized neural networks (GNNs) with time-varying delays. By utilizing generalized free-weighting matrix inequality (GFWMI) and some mathematical techniques, sufficient conditions which are dependent on the size of time delays are derived for guaranteeing the stability of GNNs. Additionally, the augmented zero equality approach (AZEA) is applied to enhance the results and eliminate the free variables. Three numerical examples show that the proposed method can be effective and provide less conservative results than previous researches.
Publisher
Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers,Springer Nature B.V,제어·로봇·시스템학회
Subject
