A delay-partitioning approach to stability analysis of discrete-time recurrent neural networks with randomly occurred nonlinearities

Authors:
Jianmin Duan;Manfeng Hu;Yongqing Yang
Affiliations:
School of Science, Jiangnan University, Wuxi, China;School of Science, Jiangnan University, Wuxi, China,Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University , Ministry of Education, Wuxi, China;School of Science, Jiangnan University, Wuxi, China,Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University , Ministry of Education, Wuxi, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

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Abstract

This paper considers the problem of stability analysis for discrete-time recurrent neural networks with randomly occurred nonlinearities (RONs) and time-varying delay. By utilizing new Lyapunov-Krasovskii functions and delay-partitioning technique, the stability criteria are proposed in terms of linear matrix inequality (LMI). We have also shown that the conservatism of the conditions is a non-increasing function of the number of delay partitions. A numerical example is provided to demonstrate the effectiveness of the proposed approach.