New stability criteria for recurrent neural networks with a time-varying delay

Authors:
Hong-Bing Zeng;Shen-Ping Xiao;Bin Liu
Affiliations:
School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou, PRC 412008 and School of Information Science and Engineering, Central South University, Changsha, PRC 410 ...;School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou, PRC 412008;School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou, PRC 412008 and School of Engineering, Australian National University, Canberra, Australia 0200
Venue:
International Journal of Automation and Computing
Year:
2011

Citing 11
Cited 1

Qualitative Analysis and Synthesis of Recurrent Neural Networks

Qualitative Analysis and Synthesis of Recurrent Neural Networks
A comparative study of two modeling approaches in neural networks

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

Neural Networks
New Lyapunov-Krasovskii functionals for global asymptotic stability of delayed neural networks

IEEE Transactions on Neural Networks
Improved exponential stability criteria for recurrent neural networks with time-varying discrete and distributed delays

International Journal of Automation and Computing
Robust stability of interval bidirectional associative memory neural network with time delays

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improved conditions for global exponential stability of recurrent neural networks with time-varying delays

IEEE Transactions on Neural Networks
New Delay-Dependent Stability Criteria for Neural Networks With Time-Varying Delay

IEEE Transactions on Neural Networks
Stability Analysis for Neural Networks With Time-Varying Interval Delay

IEEE Transactions on Neural Networks
Delay-Dependent Stability for Recurrent Neural Networks With Time-Varying Delays

IEEE Transactions on Neural Networks
Diagonal recurrent neural networks for dynamic systems control

IEEE Transactions on Neural Networks

The analysis of global input-to-state stability for piecewise affine systems with time-delay

International Journal of Automation and Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper deals with the stability of static recurrent neural networks (RNNs) with a time-varying delay. An augmented Lyapunov-Krasovskii functional is employed, in which some useful terms are included. Furthermore, the relationship among the time-varying delay, its upper bound and their difference, is taken into account, and novel bounding techniques for 1 驴 $$ \dot \tau $$ (t) are employed. As a result, without ignoring any useful term in the derivative of the Lyapunov-Krasovskii functional, the resulting delay-dependent criteria show less conservative than the existing ones. Finally, a numerical example is given to demonstrate the effectiveness of the proposed methods.