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New results on stability analysis for a class of generalized delayed neural networks

Author

Listed:
  • Chen, Yun
  • Li, Yaqi
  • Chen, Gang

Abstract

This paper addresses the stability analysis of neural networks with a time-varying delay, where the delay is periodically varying bounded function with constrained derivatives. In order to obtain less conservative stability criteria, two novel Lyapunov-Krasovskii functionals (LKFs) with delay-derivative-variation-dependent matrices are constructed. In one LKF, two delay-product terms are double utilized and more free matrices are incorporated. Additionally, a new type of the LKF with delay-derivative-variation-dependent matrices, namely, the neuronal-activation-function-based looped functional, is developed by accounting for the periodic nature of the delay function and the coupling information of the system state and the neural activation function. Then, several stability conditions of delayed neural networks are developed by combining some integral inequalities. Finally, the superiority and validity of developed stability conditions are validated on two benchmark examples.

Suggested Citation

  • Chen, Yun & Li, Yaqi & Chen, Gang, 2024. "New results on stability analysis for a class of generalized delayed neural networks," Applied Mathematics and Computation, Elsevier, vol. 469(C).
    • Handle: RePEc:eee:apmaco:v:469:y:2024:i:c:s0096300324000018
    DOI: 10.1016/j.amc.2024.128529
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