Finite-time stabilization and energy consumption estimation for delayed neural networks with bounded activation function

被引：17

作者：

Chen, Chongyang ^{[1
]}

Zhu, Song ^{[1
]}

Wang, Min ^{[1
]}

Yang, Chunyu ^{[2
]}

Zeng, Zhigang ^{[3
]}

机构：

[1] China Univ Min & Technol, Sch Math, Xuzhou 221116, Jiangsu, Peoples R China

[2] China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Jiangsu, Peoples R China

[3] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China

来源：

NEURAL NETWORKS | 2020年 / 131卷

关键词：

Nonlinear neural networks; Time delay; Finite-time stability; Energy consumption; GLOBAL EXPONENTIAL STABILITY; ADAPTIVE-CONTROL; VARYING DELAYS; FEEDBACK;

D O I：

10.1016/j.neunet.2020.07.032

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper concentrates on finite-time stabilization and energy consumption estimation for one type of delayed neural networks (DNNs) with bounded activation function. Under the bounded activation function condition and using the comparison theorem, a new switch controller is proposed to ensure the finite-time stability of the considered DNNs. Furthermore, the energy consumption produced in system controlling is estimated by inequality techniques. We generalize the previous results about the problem of finite-time stabilization and energy consumption estimation for neural networks. Ultimately, two numerical simulations are carried out to verify the validity of our results. (C) 2020 Elsevier Ltd. All rights reserved.

引用

页码：163 / 171

页数：9

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