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Dissipativity and Passivity Analysis of Markovian Jump Neural Networks with Two Additive Time-Varying Delays

Research paper by G. Nagamani, T. Radhika

Indexed on: 07 Sep '16Published on: 01 Oct '16Published in: Neural Processing Letters



Abstract

Abstract In this paper, we investigated a problem of dissipativity and passivity analysis of Markovian jump neural networks involving two additive time-varying delays. By considering proper triple integral terms in the Lyapunov–Krasovskii functional, several sufficient conditions are derived for verifying the dissipativity criteria of neural networks. The relationship between the time-varying delay and its lower and upper bounds is taken into account when estimating the upper bound of the time delay. As a result, some improved delay dissipativity criteria for neural networks with two additive time-varying delays components are proposed. The dissipativity criteria that depend on the upper bounds of the leakage time-varying delay and its derivative is given in terms of linear matrix inequalities, which can be efficiently solved via standard numerical software. Finally, three numerical examples are given to show the effectiveness of the proposed results.AbstractIn this paper, we investigated a problem of dissipativity and passivity analysis of Markovian jump neural networks involving two additive time-varying delays. By considering proper triple integral terms in the Lyapunov–Krasovskii functional, several sufficient conditions are derived for verifying the dissipativity criteria of neural networks. The relationship between the time-varying delay and its lower and upper bounds is taken into account when estimating the upper bound of the time delay. As a result, some improved delay dissipativity criteria for neural networks with two additive time-varying delays components are proposed. The dissipativity criteria that depend on the upper bounds of the leakage time-varying delay and its derivative is given in terms of linear matrix inequalities, which can be efficiently solved via standard numerical software. Finally, three numerical examples are given to show the effectiveness of the proposed results.