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The optimal state estimation for competitive neural network with time-varying delay using Local Search Algorithm

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  • Shi, Zhicheng
  • Yang, Yongqing
  • Chang, Qi
  • Xu, Xianyun

Abstract

In this paper, the optimal state estimation of competitive neural network with time-varying delay is investigated. A valid linear matrix inequality (LMI) method for neuron state estimation is proposed, sufficient conditions for the asymptotic stability of the error system are obtained. When the system is stable, further research is put forward based on the knowledge of cybernetics optimization. In particular, the Local Search Algorithm is used to optimize the parameters of state estimator. The optimal state estimator is obtained by minimizing the preset energy function. Numerical examples are included to illustrate the applicability of the proposed design method.

Suggested Citation

  • Shi, Zhicheng & Yang, Yongqing & Chang, Qi & Xu, Xianyun, 2020. "The optimal state estimation for competitive neural network with time-varying delay using Local Search Algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    • Handle: RePEc:eee:phsmap:v:540:y:2020:i:c:s0378437119317509
    DOI: 10.1016/j.physa.2019.123102
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    References listed on IDEAS

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    1. Jinliang Liu & Jie Cao & Zhiang Wu & Qiong Qi, 2014. "State estimation for complex systems with randomly occurring nonlinearities and randomly missing measurements," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(7), pages 1364-1374, July.
    2. Zhang, Dian & Cheng, Jun & Cao, Jinde & Zhang, Dan, 2019. "Finite-time synchronization control for semi-Markov jump neural networks with mode-dependent stochastic parametric uncertainties," Applied Mathematics and Computation, Elsevier, vol. 344, pages 230-242.
    3. Zhang, Hongmei & Cao, Jinde & Xiong, Lianglin, 2019. "Novel synchronization conditions for time-varying delayed Lur’e system with parametric uncertainty," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 224-236.
    4. Jiang, Ling & Cao, Jinde & Xiong, Lianglin, 2019. "Generalized multiobjective robustness and relations to set-valued optimization," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 599-608.
    5. Zhang, Hai & Ye, Miaolin & Ye, Renyu & Cao, Jinde, 2018. "Synchronization stability of Riemann–Liouville fractional delay-coupled complex neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 155-165.
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    Cited by:

    1. Adhira, B. & Nagamani, G., 2023. "Exponentially finite-time dissipative discrete state estimator for delayed competitive neural networks via semi-discretization approach," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Yang, Jin & Jian, Jigui, 2023. "Quasi-invariant and attracting sets of competitive neural networks with time-varying and infinite distributed delays," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    3. Wang, Shasha & Jian, Jigui, 2023. "Predefined-time synchronization of fractional-order memristive competitive neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

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