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Journal of Applied Mathematics
Volume 2013 (2013), Article ID 450175, 18 pages
http://dx.doi.org/10.1155/2013/450175
Research Article

Less Conservative Stability Criteria for Neutral Type Neural Networks with Mixed Time-Varying Delays

1School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
3Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu 611731, China
4College of Information Sciences and Technology, Hainan University, Haikou 570228, China

Received 20 May 2013; Accepted 7 September 2013

Academic Editor: Qiankun Song

Copyright © 2013 Kaibo Shi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper investigates the problem of dependent stability criteria for neutral type neural networks with mixed time-varying delays. Firstly, some new delay-dependent stability results are obtained by employing the more general partitioning approach and generalizing the famous Jensen inequality. Secondly, based on a new type of Lyapunov-Krasovskii functional with the cross terms of variables, less conservative stability criteria are proposed in terms of linear matrix inequalities (LMIs). Furthermore, it is the first time that the idea of second-order convex combination and the property of quadratic convex function applied to the derivation of neutral type neural networks play an important role in reducing the conservatism of the paper. Finally, four numerical examples are given to show the effectiveness and the advantage of the proposed method.