Journal Article10.1016/J.NEUCOM.2015.12.056
Delay-range-dependent passivity analysis for uncertain stochastic neural networks with discrete and distributed time-varying delays
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TL;DR: A new integral inequality proved to be less conservatism than celebrated Jensen's inequality is considered and takes fully the relationship between the terms in the Leibniz-Newton formula within the framework of linear matrix inequalities (LMIs).
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About: This article is published in Neurocomputing. The article was published on 12 Apr 2016. The article focuses on the topics: Linear matrix inequality & Stochastic neural network.
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Citations
New Criteria for Stability of Generalized Neural Networks Including Markov Jump Parameters and Additive Time Delays
TL;DR: This paper examines the problem of asymptotic stability criteria for Markovian jump generalized neural networks with successive time-varying delay components with a four-tank benchmark real-world problem and proposes a new set of delay-dependent conditions in the form of linear matrix inequalities.
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Robust stability of uncertain stochastic complex-valued neural networks with additive time-varying delays
TL;DR: The robust stability problem for a class of uncertain stochastic complex-valued neural networks (USCVNNs) with additive time-varying delays (ATDs) is discussed and some delay-dependent stability criteria are derived by converting USCVNNs into an equivalent real-valued uncertain Stochastic neural networks.
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Exponential stability and extended dissipativity criteria for generalized neural networks with interval time-varying delay signals
TL;DR: An exponential stability and extended dissipativity concept can be developed to analyze simultaneously the solutions of the exponential H∞, L 2 − L ∞, passivity, and dissipativity performance for GNNs by selecting the weighting matrices.
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Synchronization of delayed complex dynamical network with hybrid-coupling via aperiodically intermittent pinning control
TL;DR: With the help of a new established differential inequality with distributed time-delay, several useful conditions to guarantee the global exponential synchronization of the complex dynamical network with mixed delays are provided.
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An Improved Result on Dissipativity and Passivity Analysis of Markovian Jump Stochastic Neural Networks With Two Delay Components
TL;DR: This paper investigates the dissipativity and passivity of Markovian jump stochastic neural networks involving two additive time-varying delays using a Lyapunov–Krasovskii functional and introduces a generalized Finsler lemma (GFL) to reduce design conservatism.
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