New delay-dependent exponential stability for discrete-time recurrent neural networks with mixed time-delays

Authors:
Cheng-De Zheng;Chao-Ke Gong;Zhanshan Wang
Affiliations:
School of Science, Dalian Jiaotong University, Dalian, 116028, China;School of Science, Dalian Jiaotong University, Dalian, 116028, China;School of Information Science and Engineering, Northeastern University, Shenyang, 110004, China
Venue:
International Journal of Innovative Computing and Applications
Year:
2013

Citing 15
Cited 0

Stability of Time-Delay Systems

Stability of Time-Delay Systems
Exponential stability preservation in discrete-time analogues of artificial neural networks with distributed delays

Journal of Computational and Applied Mathematics
Improved delay-dependent exponential stability criteria for discrete-time recurrent neural networks with time-varying delays

Neurocomputing
Letters: State estimation for discrete-time neural networks with time-varying delays

Neurocomputing
Asymptotic stability for neural networks with mixed time-delays: The discrete-time case

Neural Networks
Robust stability analysis of generalized neural networks with multiple discrete delays and multiple distributed delays

Neurocomputing
A new approach to stability analysis of discrete-time recurrent neural networks with time-varying delay

Neurocomputing
New results on robust exponential stability for discrete recurrent neural networks with time-varying delays

Neurocomputing
Letters: New delay-dependent stability results for discrete-time recurrent neural networks with time-varying delay

Neurocomputing
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
Improved exponential stability criteria for discrete-time neural networks with time-varying delay

Neurocomputing
Global Asymptotical Stability of Recurrent Neural Networks With Multiple Discrete Delays and Distributed Delays

IEEE Transactions on Neural Networks
Global Asymptotic Stability of Delayed Cellular Neural Networks

IEEE Transactions on Neural Networks
Global Asymptotic Stability of Recurrent Neural Networks With Multiple Time-Varying Delays

IEEE Transactions on Neural Networks
Robust Stability Analysis for Interval Cohen–Grossberg Neural Networks With Unknown Time-Varying Delays

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

The existence, uniqueness and globally exponential stability is investigated for a class of discrete-time recurrent neural networks with discrete and bounded distributed delays. The activation functions are required to be neither bounded nor monotonic. By introducing triple-sum terms, a new Lyapunov-Krasovskii functional is constructed. After using the homeomorphism mapping principle, discrete Jensen inequality and generalised discrete Jensen inequality, a linear matrix inequality LMI approach is developed to establish delay-dependent sufficient conditions for exponential stability of the discrete-time neural networks. As the obtained conditions are expressed in terms of LMIs, the feasibility can be easily checked by using the numerically efficient MATLAB LMI toolbox. Two numerical examples are also given to show the effectiveness of our results.