A delay partitioning approach to delay-dependent stability analysis for neutral type neural networks with discrete and distributed delays

  • Authors:

  • S. Lakshmanan;Ju H. Park;H. Y. Jung;O. M. Kwon;R. Rakkiyappan

  • Affiliations:

  • Department of Electrical Engineering/Information and Communication Engineering, Yeungnam University, 214-1 Dae-dong, Kyongsan 712-749, Republic of Korea;Department of Electrical Engineering/Information and Communication Engineering, Yeungnam University, 214-1 Dae-dong, Kyongsan 712-749, Republic of Korea;Department of Electrical Engineering/Information and Communication Engineering, Yeungnam University, 214-1 Dae-dong, Kyongsan 712-749, Republic of Korea;School of Electrical Engineering, Chungbuk National University, 52 Naesudong-ro, Heungduk-gu, Cheongju 361-763, Republic of Korea;Department of Mathematics, Bharathiar University, Coimbatore 641 046, Tamilnadu, India

  • Venue:
  • Neurocomputing
  • Year:
  • 2013

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Abstract

This paper is concerned with the stability analysis of neutral type neural networks with discrete and distributed delays. Some improved delay-dependent stability results are established by using a delay partitioning approach for the networks. By employing a new type of Lyapunov-Krasovskii functionals, new delay-dependent stability criteria are derived. All the criteria are expressed in terms of linear matrix inequalities (LMIs), which can be solved efficiently by using standard convex optimization algorithms. Finally, numerical examples are given to illustrate the less conservatism of the proposed method.