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

Kaibo Shi | Zendy; Hong Zhu | Zendy; Shouming Zhong | Zendy; Yong Zeng | Zendy; Yuping Zhang | Zendy; Liang Li | Zendy

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

Author(s) -

Kaibo Shi,

Hong Zhu,

Shouming Zhong,

Yong Zeng,

Yuping Zhang,

Liang Li

Publication year - 2013

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2013/450175

Subject(s) - stability (learning theory) , type (biology) , mathematics , artificial neural network , linear matrix inequality , convex combination , quadratic equation , function (biology) , stability conditions , regular polygon , convex function , property (philosophy) , convex optimization , mathematical optimization , control theory (sociology) , computer science , control (management) , artificial intelligence , ecology , philosophy , statistics , geometry , discrete time and continuous time , epistemology , machine learning , evolutionary biology , biology

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 criteriaare 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

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