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Passivity Analysis for Uncertain BAM Neural Networks with Leakage, Discrete and Distributed Delays Using Novel Summation Inequality

Sowmiya Chandran, Raja Ramachandran, Jinde Cao*, Ravi. P. Agarwal, and Grienggrai Rajchakit
International Journal of Control, Automation, and Systems, vol. 17, no. 8, pp.2114-2124, 2019

Abstract : This proposed research work is devoted to the problem of passivity analysis for uncertain BAM neural networks with leakage, discrete and distributed delays using novel summation inequality. The uniqueness of this proposal is from the deliberation of an advanced inequality (i.e., novel summation inequality) which is novelty than the famous Jensen inequality engaged in the framework of discrete-time neural networks systems. For the investigation of continuous-time neural networks, Wirtinger-based integral inequality was currently engaged whereas novel summation inequality is for discrete-time neural networks. With the assistance of Lyapunov-Krasovskii functional, some conditions are derived and entrenched in terms of linear matrix inequalities which can be easily checked by some available software packages. Two benchmark examples are proposed and lead better upper bounds for the tolerable time delay than existing literatures to show the fruitfulness and efficacy of the proposed work.

Keyword : BAM neural networks, leakage delay, linear matrix inequality, mixed delay, passivity, summation inequality, time-varying delay, uncertain.

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