New Results on Passivity Analysis of Stochastic Neural Networks with Time-Varying Delay and Leakage Delay
YaJun Li, Zhaowen Huang
TL;DR
This paper improves the analysis of stability in neural networks with time delays using advanced mathematical techniques.
Contribution
A novel Lyapunov functional and free-weighting matrix approach reduce conservatism in stability analysis for stochastic neural networks.
Findings
Delay-dependent stability conditions are derived using integral inequality and stochastic analysis.
Introducing adjustable parameters leads to less conservative results by utilizing more time delay information.
Simulations demonstrate the impact of leakage delay on the stability of stochastic neural networks.
Abstract
The passivity problem for a class of stochastic neural networks systems (SNNs) with varying delay and leakage delay has been further studied in this paper. By constructing a more effective Lyapunov functional, employing the free-weighting matrix approach, and combining with integral inequality technic and stochastic analysis theory, the delay-dependent conditions have been proposed such that SNNs are asymptotically stable with guaranteed performance. The time-varying delay is divided into several subintervals and two adjustable parameters are introduced; more information about time delay is utilised and less conservative results have been obtained. Examples are provided to illustrate the less conservatism of the proposed method and simulations are given to show the impact of leakage delay on stability of SNNs.
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Taxonomy
TopicsNeural Networks Stability and Synchronization · Advanced Memory and Neural Computing · stochastic dynamics and bifurcation