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New Optimization Approach of State Estimation for Neural Networks with Mixed Delays

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Abstract

This article investigates the H∞ state estimation for neural networks with both discrete and distributed time-delays. A new Lyapunov–Krasovskii functionals (LKF) is established by including two novel delay-product-type terms, multiple integral terms and more general activation function. Then, by utilizing the generalized free-weighting matrix inequality and dividing the boundary of activation function into two parts, new sufficient conditions are derived such that the estimation error system is asymptotically stable with desired H∞ performance level. Finally, the advantages of presented method are demonstrated through three numerical examples.

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Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgments

This work was supported partially by NSFC under Grant 61973105, the Innovation Scientists and Technicians Troop Construction Projects of Henan Province under Grant CXTD2016054, Zhongyuan High Level Talents Special Support Plan under Grant ZYQR201912031, the Fundamental Research Funds for the Universities of Henan Province under Grant NSFRF170501, and in part by Innovative Scientists and Technicians Team of Henan Provincial High Education under Grant 20IRTSTHN019.

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  1. School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, 454000, China

    Haibo Liu, Wei Qian & Yunji Zhao

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  1. Haibo Liu
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  2. Wei Qian
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Correspondence to Wei Qian.

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Liu, H., Qian, W. & Zhao, Y. New Optimization Approach of State Estimation for Neural Networks with Mixed Delays. Circuits Syst Signal Process 41, 3777–3797 (2022). https://doi.org/10.1007/s00034-022-01980-1

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  • DOI: https://doi.org/10.1007/s00034-022-01980-1

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