Fuzzy-Model-Based H∞ Pinning Synchronization for Coupled Neural Networks Subject to Reaction-Diffusion

被引:35
作者
Wang, Jing [1 ]
Wang, Xuelian [1 ]
Xie, Nenggang [2 ]
Xia, Jianwei [3 ]
Shen, Hao [1 ]
机构
[1] Anhui Univ Technol, Sch Elect & Informat Engn, Maanshan 243002, Peoples R China
[2] Anhui Univ Technol, Sch Management Sci & Engn, Maanshan 243002, Peoples R China
[3] Liaocheng Univ, Sch Math Sci, Liaocheng 252059, Shandong, Peoples R China
基金
中国国家自然科学基金;
关键词
Adaptive pinning control synchronization; a fuzzy-model-based method; coupled neural networks (CNNs); reaction-diffusion; SYSTEMS;
D O I
10.1109/TFUZZ.2020.3036697
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This article investigates the H-infinity synchronization problem for fuzzy coupled neural networks subject to reaction-diffusion. An available control method, namely, the adaptive pinning control strategy, is employed. In view of such a method, one may accomplish control objectives by controlling a small number of nodes instead of all nodes, and in this regard, it is possible to reduce the control cost to some extent, and the method can adaptively adjust the coupling strength as well. Furthermore, a novel inequality is introduced, which can ensure that the developed results are less conservative compared with some existing ones of dealing with the reaction-diffusion terms. Then, through the utilization of fuzzy set theory together with Lyapunov stability theory, some sufficient conditions with the ability to ensure the H-infinity performance level of the resulting synchronization error system are deduced. Finally, an illustrative example is presented to show the advantages and effectiveness of the proposed methods.
引用
收藏
页码:248 / 257
页数:10
相关论文
共 53 条
[21]   An extended synchronization analysis for memristor-based coupled neural networks via aperiodically intermittent control [J].
Luo, Mengzhuo ;
Cheng, Jun ;
Liu, Xinzhi ;
Zhong, Shouming .
APPLIED MATHEMATICS AND COMPUTATION, 2019, 344 :163-182
[22]   Pinning observability of competitive neural networks with different time-constants [J].
Meyer-Base, A. ;
Amani, A. Moradi ;
Meyer-Base, U. ;
Foo, S. ;
Stadlbauer, A. ;
Yu, W. .
NEUROCOMPUTING, 2019, 329 :97-102
[23]   Synchronization of Fractional Hyperchaotic Rabinovich Systems via Linear and Nonlinear Control with an Application to Secure Communications [J].
Ouannas, Adel ;
Bendoukha, Samir ;
Volos, Christos ;
Boumaza, Nouri ;
Karouma, Abdulrahman .
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2019, 17 (09) :2211-2219
[24]   Impulsive synchronization of coupled delayed neural networks with actuator saturation and its application to image encryption [J].
Ouyang, Deqiang ;
Shao, Jie ;
Jiang, Haijun ;
Nguang, Sing Kiong ;
Shen, Heng Tao .
NEURAL NETWORKS, 2020, 128 :158-171
[25]   Stable fuzzy logic control of a general class of chaotic systems [J].
Precup, Radu-Emil ;
Tomescu, Marius L. .
NEURAL COMPUTING & APPLICATIONS, 2015, 26 (03) :541-550
[26]   Fuzzy control for Electric Power Steering System with assist motor current input constraints [J].
Saifia, D. ;
Chadli, M. ;
Karimi, H. R. ;
Labiod, S. .
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2015, 352 (02) :562-576
[27]   H∞ Filtering for Fuzzy Jumping Genetic Regulatory Networks With Round-Robin Protocol: A Hidden-Markov-Model-Based Approach [J].
Shen, Hao ;
Men, Yunzhe ;
Cao, Jinde ;
Park, Ju H. .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2020, 28 (01) :112-121
[28]   Exponential H∞ Filtering for Continuous-Time Switched Neural Networks Under Persistent Dwell-Time Switching Regularity [J].
Shen, Hao ;
Huang, Zhengguo ;
Cao, Jinde ;
Park, Ju H. .
IEEE TRANSACTIONS ON CYBERNETICS, 2020, 50 (06) :2440-2449
[29]   Global exponential estimates for uncertain Markovian jump neural networks with reaction-diffusion terms [J].
Shen, Hao ;
Huang, Xia ;
Zhou, Jianping ;
Wang, Zhen .
NONLINEAR DYNAMICS, 2012, 69 (1-2) :473-486
[30]   Delay-Dependent Global Exponential Stability for Delayed Recurrent Neural Networks [J].
Sheng, Yin ;
Shen, Yi ;
Zhu, Mingfu .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017, 28 (12) :2974-2984