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

Author

Listed:
  • Kaibo Shi
  • Hong Zhu
  • Shouming Zhong
  • Yong Zeng
  • Yuping Zhang
  • Li Liang

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.

Suggested Citation

  • Kaibo Shi & Hong Zhu & Shouming Zhong & Yong Zeng & Yuping Zhang & Li Liang, 2013. "Less Conservative Stability Criteria for Neutral Type Neural Networks with Mixed Time-Varying Delays," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-18, November.
    • Handle: RePEc:hin:jnljam:450175
    DOI: 10.1155/2013/450175
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