A review of dynamics analysis of neural networks and applications in creation psychology

被引:0
作者
Yin, Xiangwen [1 ]
机构
[1] Shandong Normal Univ, Sch Chinese Language & Literature, Jinan 250014, Peoples R China
来源
ELECTRONIC RESEARCH ARCHIVE | 2023年 / 31卷 / 05期
关键词
review; synchronization; stability; stabilization; memristive neural networks; reaction-diffusion neural networks; creation psychology; TIME-VARYING DELAYS; GLOBAL EXPONENTIAL STABILITY; ALMOST-PERIODIC SOLUTIONS; MIXED DELAYS; IMPULSIVE SYNCHRONIZATION; FEEDBACK SYNCHRONIZATION; ASYMPTOTIC STABILITY; STABILIZATION; MODEL; SYSTEM;
D O I
10.3934/era.2023132
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
The synchronization problem and the dynamics analysis of neural networks have been thoroughly explored, and there have been many interesting results. This paper presents a review of the issues of synchronization problem, the periodic solution and the stability/stabilization with emphasis on the memristive neural networks and reaction-diffusion neural networks. First, this paper introduces the origin and development of neural networks. Then, based on different types of neural networks, some synchronization problems and the design of the controllers are introduced and summarized in detail. Some results of the periodic solution are discussed according to different neural networks, including bi-directional associative memory (BAM) neural networks and cellular neural networks. From the perspective of memristive neural networks and reaction-diffusion neural networks, some results of stability and stabilization are reviewed comprehensively with latest progress. Based on a review of dynamics analysis of neural networks, some applications in creation psychology are also introduced. Finally, the conclusion and the future research directions are provided.
引用
收藏
页码:2595 / 2625
页数:31
相关论文
共 163 条
[11]   Aperiodic intermittent pinning control for exponential synchronization of memristive neural networks with time-varying delays [J].
Cai, Shuiming ;
Li, Xiaojing ;
Zhou, Peipei ;
Shen, Jianwei .
NEUROCOMPUTING, 2019, 332 :249-258
[12]   Finite-Time Stabilization of Delayed Memristive Neural Networks: Discontinuous State-Feedback and Adaptive Control Approach [J].
Cai, Zuowei ;
Huang, Lihong .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 29 (04) :856-868
[13]   Global asymptotic stability of a general class of recurrent neural networks with time-varying delays [J].
Cao, J ;
Wang, J .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2003, 50 (01) :34-44
[14]   New results concerning exponential stability and periodic solutions of delayed cellular neural networks [J].
Cao, JD .
PHYSICS LETTERS A, 2003, 307 (2-3) :136-147
[15]   Global exponential stability and periodic solutions of delayed cellular neural networks [J].
Cao, JD .
JOURNAL OF COMPUTER AND SYSTEM SCIENCES, 2000, 60 (01) :38-46
[16]   Almost Periodicity in Impulsive Fractional-Order Reaction-Diffusion Neural Networks With Time-Varying Delays [J].
Cao, Jinde ;
Stamov, Gani ;
Stamova, Ivanka ;
Simeonov, Stanislav .
IEEE TRANSACTIONS ON CYBERNETICS, 2021, 51 (01) :151-161
[17]   Global exponential synchronization of delayed memristive neural networks with reaction-diffusion terms [J].
Cao, Yanyi ;
Cao, Yuting ;
Guo, Zhenyuan ;
Huang, Tingwen ;
Wen, Shiping .
NEURAL NETWORKS, 2020, 123 :70-81
[18]   Existence and stability of almost periodic solution for BAM neural networks with delays [J].
Chen, AP ;
Huang, LH ;
Cao, JD .
APPLIED MATHEMATICS AND COMPUTATION, 2003, 137 (01) :177-193
[19]   Pinning impulsive synchronization for stochastic reaction-diffusion dynamical networks with delay [J].
Chen, Huabin ;
Shi, Peng ;
Lim, Cheng-Chew .
NEURAL NETWORKS, 2018, 106 :281-293
[20]   On the periodic dynamics of memristor-based neural networks with time-varying delays [J].
Chen, Jiejie ;
Zeng, Zhigang ;
Jiang, Ping .
INFORMATION SCIENCES, 2014, 279 :358-373