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

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

Chaos, Solitons & Fractals, 2023, vol. 172, issue C

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.

Keywords: Switched generalized neural networks; Discrete-time domain; Finite-time analysis; Unstable subnetworks; Peak-to-peak performance (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:172:y:2023:i:c:s0960077923004563

DOI: 10.1016/j.chaos.2023.113555

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