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Abstract This paper studies the stability problem for neural networks with time-varying delay. A novel Lyapunov-Krasovskii functional (LKF) is constructed that contains a delay-product-type (DPT) functional and a multiple-integral-type (MIT)… Click to show full abstract
Abstract This paper studies the stability problem for neural networks with time-varying delay. A novel Lyapunov-Krasovskii functional (LKF) is constructed that contains a delay-product-type (DPT) functional and a multiple-integral-type (MIT) functional. Therein, the DPT functional covers some existing ones as its special cases. In order to estimate the derivative of the MIT functional, an auxiliary function-based multiple integral inequality (AFMII) is presented, which can treat some existing results as its special cases. Based on these ingredients, a novel stability condition is obtained for neural networks with time-varying delay. A numerical example is given to illustrate the advantages of the stability condition.
Journal Title: Neurocomputing
Year Published: 2020
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