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Stability Analysis of Pseudo-Almost Periodic Solution for a Class of Cellular Neural Network with D Operator and Time-Varying Delays

Author

Listed:
  • Weide Liu

    (School of Aeronautics and Astronautics, Sun Yat-sen University, Guangzhou 510275, China)

  • Jianliang Huang

    (School of Aeronautics and Astronautics, Sun Yat-sen University, Guangzhou 510275, China)

  • Qinghe Yao

    (School of Aeronautics and Astronautics, Sun Yat-sen University, Guangzhou 510275, China)

Abstract

Cellular neural networks with D operator and time-varying delays are found to be effective in demonstrating complex dynamic behaviors. The stability analysis of the pseudo-almost periodic solution for a novel neural network of this kind is considered in this work. A generalized class neural networks model, combining cellular neural networks and the shunting inhibitory neural networks with D operator and time-varying delays is constructed. Based on the fixed-point theory and the exponential dichotomy of linear equations, the existence and uniqueness of pseudo-almost periodic solutions are investigated. Through a suitable variable transformation, the globally exponentially stable sufficient condition of the cellular neural network is examined. Compared with previous studies on the stability of periodic solutions, the global exponential stability analysis for this work avoids constructing the complex Lyapunov functional. Therefore, the stability criteria of the pseudo-almost periodic solution for cellular neural networks in this paper are more precise and less conservative. Finally, an example is presented to illustrate the feasibility and effectiveness of our obtained theoretical results.

Suggested Citation

  • Weide Liu & Jianliang Huang & Qinghe Yao, 2021. "Stability Analysis of Pseudo-Almost Periodic Solution for a Class of Cellular Neural Network with D Operator and Time-Varying Delays," Mathematics, MDPI, vol. 9(16), pages 1-24, August.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:16:p:1951-:d:614895
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    References listed on IDEAS

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    1. Amdouni, Manel & Chérif, Farouk, 2018. "The pseudo almost periodic solutions of the new class of Lotka–Volterra recurrent neural networks with mixed delays," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 79-88.
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    Cited by:

    1. Shen, Shiping & Meng, Xiaofang, 2023. "Finite-time stability of almost periodic solutions of Clifford-valued RNNs with time-varying delays and D operator on time scales," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    2. Gao, Jin & Dai, Lihua & Jiang, Hongying, 2023. "Stability analysis of pseudo almost periodic solutions for octonion-valued recurrent neural networks with proportional delay," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).

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