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

被引：4

作者：

Liu, Haibo ^{[1
]}

Qian, Wei ^{[1
]}

Zhao, Yunji ^{[1
]}

机构：

[1] Henan Polytech Univ, Sch Elect Engn & Automat, Jiaozuo 454000, Henan, Peoples R China

来源：

CIRCUITS SYSTEMS AND SIGNAL PROCESSING | 2022年 / 41卷 / 07期

关键词：

Neural networks; Mixed delays; H-infinity state estimation; Delay-product-type functional; TIME-VARYING DELAY; STABILITY ANALYSIS; CRITERIA;

D O I：

10.1007/s00034-022-01980-1

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This article investigates the H-infinity 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-infinity performance level. Finally, the advantages of presented method are demonstrated through three numerical examples.

引用

页码：3777 / 3797

页数：21

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