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

Authors:
Xun-Lin Zhu;Youyi Wang;Guang-Hong Yang
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore and School of Computer and Communication Engineering, Zhengzhou University of Light I ...;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004, China
Venue:
Neurocomputing
Year:
2009

Citing 12
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Abstract

This paper studies the problem of stability analysis for discrete-time recurrent neural networks (DRNNs) with time-varying delays. By using the discrete Jensen inequality and the sector bound conditions, a new less conservative delay-dependent stability criterion is established in terms of linear matrix inequalities (LMIs) under a weak assumption on the activation functions. By using a delay decomposition method, a further improved stability criterion is also derived. It is shown that the newly obtained results are less conservative than the existing ones. Meanwhile, the computational complexity of the newly obtained stability conditions is reduced since less variables are involved. A numerical example is given to illustrate the effectiveness and the benefits of the proposed method.