Abstract
This paper discusses the issue of partial-neurons-based \(H_{\infty }\) state estimation for time-varying recurrent neural networks subject to randomly occurring time delays under variance constraint index. The measurement outputs are allowed to be available only at certain neurons. In addition, a random variable is introduced to model the randomly occurring time delays with certain occurrence probability. The aim is to propose the non-augmented partial-neurons-based state estimation strategy. Accordingly, some sufficient conditions are given to ensure two indices including the pre-determined \(H_{\infty }\) performance index and the error variance boundedness via the stochastic analysis approach. Finally, a simulation example is used to demonstrate the feasibility of presented partial-neurons-based \(H_{\infty }\) state estimation algorithm.
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Jun Hu and Yan Gao wrote the main manuscript text and prepared figures. Cai Chen did the simulation. All authors reviewed the manuscript
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This work was supported in part by the National Natural Science Foundation of China under Grant 12171124, the Natural Science Foundation of Heilongjiang Province of China under Grant ZD2022F003, the Postdoctoral Science Foundation of Heilongjiang Province of China under Grant LBH-Z22199, the Fundamental Research Funds in Heilongjiang Provincial Universities of China under Grant 135509121, the Educational Research Project of the Qiqihar University of China under Grant YB201904, and the Alexander von Humboldt Foundation of Germany.
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Hu, J., Gao, Y., Chen, C. et al. Partial-Neurons-Based \(H_{\infty }\) State Estimation for Time-Varying Neural Networks Subject to Randomly Occurring Time Delays under Variance Constraint. Neural Process Lett 55, 8285–8307 (2023). https://doi.org/10.1007/s11063-023-11312-2
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DOI: https://doi.org/10.1007/s11063-023-11312-2