Abstract
This paper studies the problem of exponential passivity for neutral stochastic neural networks (NSNN) with leakage delay and Markovian jump. The Markovian jump has partially unknown transition probabilities (PUTPs). By utilizing the Itô differential rule, choosing a suitable Lyapunov–Krasovskii functional and combining with the inequality technique, the sufficient delay-dependent exponential passivity criteria are obtained. These sufficient conditions are provided in the form of linear matrix inequalities (LMIs), which can be easily solved by LMI toolbox in Matlab. Finally, two simulated numerical examples are discussed in detail to illustrate the effectiveness of the established results.
Highlights
During the past few decades, neural networks (NN) with time delays have found wide applications such as image processing, fixed-point computation, pattern recognition, associative memory, and so on
It is more difficult to deal with neutral-type neural networks (NTNN) comparing to the traditional delayed neural networks
[39] studied the exponential passivity problem (EPP) of Markovian jumping stochastic NN with leakage and distributed delays, and some delay-dependent sufficient conditions were obtained by the Lyapunov stability theory and the free-weighting matrix approach
Summary
During the past few decades, neural networks (NN) with time delays have found wide applications such as image processing, fixed-point computation, pattern recognition, associative memory, and so on. 3 Main results we derive the delay-dependent exponential passivity criteria for Markovian jumping NSNN with mixed and leakage delays in (8) with partially unknown transition probabilities.
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