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Stability Analysis for Neural Networks with Time-Varying Delay

In: Dynamic Systems with Time Delays: Stability and Control

Author

Listed:
  • Ju H. Park

    (Yeungnam University, Department of Electrical Engineering)

  • Tae H. Lee

    (Chonbuk National University, Division of Electronic Engineering)

  • Yajuan Liu

    (North China Electric Power University, Control and Computer Engineering)

  • Jun Chen

    (Jiangsu Normal University, School of Electrical Engineering and Automation)

Abstract

This chapter is devoted to the study of the stability of time-delay neural networks in both continuous and discrete contexts. Augmented Lyapunov–Krasovskii (L–K) functionals are deliberately constructed for both continuous and discrete cases, in which state and delay information of neural networks is fully taken into account. During the process of dealing with the time derivative (or the forward difference) of L–K functionals, the integral (or summation) inequalities are employed to estimate integral (or summation) terms. Consequently, more relaxed conditions are derived in the forms of linear matrix inequalities. Several numerical examples are presented to show the effectiveness of the proposed approach.

Suggested Citation

  • Ju H. Park & Tae H. Lee & Yajuan Liu & Jun Chen, 2019. "Stability Analysis for Neural Networks with Time-Varying Delay," Springer Books, in: Dynamic Systems with Time Delays: Stability and Control, chapter 6, pages 155-176, Springer.
    • Handle: RePEc:spr:sprchp:978-981-13-9254-2_6
    DOI: 10.1007/978-981-13-9254-2_6
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