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Finite-time peak-to-peak analysis for switched generalized neural networks comprised of finite-time unstable subnetworks

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  • Sang, Hong
  • Zhao, Ying
  • Wang, Peng
  • Wang, Yuzhong
  • Yu, Shuanghe
  • Dimirovski, Georgi M.

Abstract

This research is concerned with finite-time stability and peak-to-peak performance analysis for the discrete-time switched generalized neural networks (SGNNs) with time-varying delay. Compared with the reported results, each individual subnetwork of the SGNNs is considered to be finite-time unstable in the present study. To accomplish the anticipatory objective, the quasi-time-dependent Lyapunov–Krasovskii functional is constructed, and the associated sufficient conditions are simultaneously formulated to confirm that the disturbance-free SGNNs are finite-time stable when the subnetwork satisfies a certain switching time interval. In addition, a prescribed disturbance attenuation level is also achieved for the perturbed SGNNs in the sense of peak-to-peak performance. Finally, the provided simulation example corroborates the effectiveness and applicability of the established finite-time analysis framework in the absence of finite-time stable subnetworks.

Suggested Citation

  • Sang, Hong & Zhao, Ying & Wang, Peng & Wang, Yuzhong & Yu, Shuanghe & Dimirovski, Georgi M., 2023. "Finite-time peak-to-peak analysis for switched generalized neural networks comprised of finite-time unstable subnetworks," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    • Handle: RePEc:eee:chsofr:v:172:y:2023:i:c:s0960077923004563
    DOI: 10.1016/j.chaos.2023.113555
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    References listed on IDEAS

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