Dissipative analysis of delayed neural networks based on the negative definite lemma of cubic functions

Abstract

Dissipative analysis about delayed neural networks is explored in the research. Firstly, the Firstly, the strengthened Lyapunov-Krasovskii functional (LKF) has been built. After that, the terms having time-varying delay cubic are then formed in the LKF’s derivative by disassembling the partial integral terms in the functional into the terms that contain time-varying delay. By using the negative definite lemma of cubic function to determine its negative qualitativeness, the low conservative dissipation condition of $({\mathcal{Q}},{\mathcal{S}},{\mathcal{R}})$-γ-neural network is obtained. The developed criterion’s superiority and effectiveness is demonstrated by the numerical example at last.