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Stability analysis of delayed neural networks based on a relaxed delay-product-type Lyapunov functional

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Abstract This paper deals with the stability analysis of delayed neural networks. A necessary and sufficient condition for positivity or negativity of the high-order polynomial over a finite interval is… Click to show full abstract

Abstract This paper deals with the stability analysis of delayed neural networks. A necessary and sufficient condition for positivity or negativity of the high-order polynomial over a finite interval is derived. An appropriate Lyapunov-Krasovskii functional (LKF), including a relax delay-product-type Lyapunov functional, is constructed. The necessary and sufficient condition of the polynomial inequality and a relaxed delay-product-type Lyapunov functional are employed to derive less conservative stability criteria. Finally, three commonly used numerical examples are presented to demonstrate the effectiveness and less conservativeness of the proposed method.

Keywords: delay product; type lyapunov; stability; product type; lyapunov functional

Journal Title: Neurocomputing
Year Published: 2021

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