Dynamics of a two-neuron hopfield neural network: Memristive synapse and autapses and impact of fractional order

被引:12
作者
Ramakrishnan, Balamurali [1 ]
Wang, Zhen [2 ]
Natiq, Hayder [3 ,4 ]
Pal, Nikhil [5 ]
Rajagopal, Karthikeyan [1 ]
Jafari, Sajad [6 ,7 ]
机构
[1] Chennai Inst Technol, Ctr Nonlinear Syst, Chennai, India
[2] Yanan Univ, Sch Math & Comp Sci, Yanan 716000, Peoples R China
[3] Minist Higher Educ & Sci Res, Baghdad 10024, Iraq
[4] Imam Jaafar Al Sadiq Univ, Coll Informat Technol, Dept Comp Technol Engn, Baghdad, Iraq
[5] Visva Bharati, Dept Math, Santini Ketan 731235, West Bengal, India
[6] Amirkabir Univ Technol, Hlth Technol Res Inst, Tehran 1591634311, Iran
[7] Amirkabir Univ Technol, Dept Biomed Engn, Tehran 1591634311, Iran
关键词
Hopfield neural network; Memristive synapse; Memristive autapse; Fractional order; SYSTEM; ANALOG; MODEL;
D O I
10.1016/j.aeue.2024.155506
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
There are numerous studies on Hopfield neural networks with electromagnetic induction using memristors in either autaptic or synaptic connections. In this study, we explore a novel scenario where all connections are influenced by electromagnetic induction. We investigate and compare the network's dynamics with one memristive autapse, two memristive autapses, and a memristive synapse. The results indicate that having two memristive autapses instead of one increases the dynamical range, leading to chaotic dynamics in unequal autaptic strengths. In contrast, in the presence of the memristive synapse, chaos can emerge only in very strong synaptic strength. Using fractional-order derivatives can transform the periodic attractor of the integer-order model into a chaotic one in some parameters. Furthermore, incorporating more memristors leads to chaos at lower fractional orders.
引用
收藏
页数:8
相关论文
共 50 条
[1]  
Atangana A, 2013, A note on fractional order derivatives and table of fractional derivatives of some special functions
[2]   Hopfield neural networks for parametric identification of dynamical systems [J].
Atencia, M ;
Joya, G ;
Sandoval, F .
NEURAL PROCESSING LETTERS, 2005, 21 (02) :143-152
[3]   Memristive-cyclic Hopfield neural network: spatial multi-scroll chaotic attractors and spatial initial-offset coexisting behaviors [J].
Bao, Han ;
Chen, Zhuguan ;
Chen, Mo ;
Xu, Quan ;
Bao, Bocheng .
NONLINEAR DYNAMICS, 2023, 111 (24) :22535-22550
[4]   Offset-Control Plane Coexisting Behaviors in Two-Memristor-Based Hopfield Neural Network [J].
Bao, Han ;
Hua, Mengjie ;
Ma, Jun ;
Chen, Mo ;
Bao, Bocheng .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2023, 70 (10) :10526-10535
[5]   Recent advancement of fractional calculus and its applications in physical systems [J].
Boulaaras, Salah ;
Jan, Rashid ;
Pham, Viet-Thanh .
EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS, 2023, 232 (14-15) :2347-2350
[6]   Effects of bias current and control of multistability in 3D hopfield neural network [J].
Boya, Bertrand Frederick Boui A. ;
Ramakrishnan, Balamurali ;
Effa, Joseph Yves ;
Kengne, Jacques ;
Rajagopal, Karthikeyan .
HELIYON, 2023, 9 (02)
[7]   ANN-Based Adaptive Control of Robotic Manipulators With Friction and Joint Elasticity [J].
Chaoui, Hicham ;
Sicard, Pierre ;
Gueaieb, Wail .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2009, 56 (08) :3174-3187
[8]   Memristive bi-neuron Hopfield neural network with coexisting symmetric behaviors [J].
Chen, Chengjie ;
Min, Fuhong .
EUROPEAN PHYSICAL JOURNAL PLUS, 2022, 137 (07)
[9]   Memristive electromagnetic induction effects on Hopfield neural network [J].
Chen, Chengjie ;
Min, Fuhong ;
Zhang, Yunzhen ;
Bao, Bocheng .
NONLINEAR DYNAMICS, 2021, 106 (03) :2559-2576
[10]   DC-Offset Strategy for Controlling Hidden and Multistable Behaviors in Physical Circuits [J].
Chen, Mo ;
Wang, Ankai ;
Wu, Huagan ;
Chen, Bei ;
Xu, Quan .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2024, 71 (08) :9417-9425