Neural networks letter: Robustness analysis for connection weight matrices of global exponential stability of stochastic recurrent neural networks

Authors:
Song Zhu;Yi Shen
Affiliations:
College of Sciences, China University of Mining and Technology, Xuzhou, 221116, China;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
Venue:
Neural Networks
Year:
2013

Citing 14
Cited 1

Global stability analysis in delayed Hopfield neural network models

Neural Networks
Delay-dependent exponential stability analysis of delayed neural networks: an LMI approach

Neural Networks
Global exponential stability of delayed Hopfield neural networks

Neural Networks
Global exponential stability of generalized recurrent neural networks with discrete and distributed delays

Neural Networks
Neural networks letter: Novel stability of cellular neural networks with interval time-varying delay

Neural Networks
Global asymptotic stability and robust stability of a class of Cohen-Grossberg neural networks with mixed delays

IEEE Transactions on Circuits and Systems Part I: Regular Papers
Robust Stability of Switched Cohen–Grossberg Neural Networks With Mixed Time-Varying Delays

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An analysis of global asymptotic stability of delayed cellular neural networks

IEEE Transactions on Neural Networks
An improved global asymptotic stability criterion for delayed cellular neural networks

IEEE Transactions on Neural Networks
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays

IEEE Transactions on Neural Networks
Global Asymptotical Stability of Recurrent Neural Networks With Multiple Discrete Delays and Distributed Delays

IEEE Transactions on Neural Networks
Noise-Induced Stabilization of the Recurrent Neural Networks With Mixed Time-Varying Delays and Markovian-Switching Parameters

IEEE Transactions on Neural Networks
Stability Analysis of Markovian Jumping Stochastic Cohen–Grossberg Neural Networks With Mixed Time Delays

IEEE Transactions on Neural Networks
An Improved Algebraic Criterion for Global Exponential Stability of Recurrent Neural Networks With Time-Varying Delays

IEEE Transactions on Neural Networks

A new condition for robust stability of uncertain neural networks with time delays

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper analyzes the robustness of global exponential stability of stochastic recurrent neural networks (SRNNs) subject to parameter uncertainty in connection weight matrices. Given a globally exponentially stable stochastic recurrent neural network, the problem to be addressed here is how much parameter uncertainty in the connection weight matrices that the neural network can remain to be globally exponentially stable. We characterize the upper bounds of the parameter uncertainty for the recurrent neural network to sustain global exponential stability. A numerical example is provided to illustrate the theoretical result.