Fractional-order bi-Hopfield neuron coupled via a multistable memristor: Complex neuronal dynamic analysis and implementation with microcontroller

作     者：Tamba, Victor Kamdoum Biamou, Arsene Loic Mbanda Pham, Viet-Thanh Grassi, Giuseppe Tagne, Francois Kapche Takougang, Armand Cyrille Nzeukou 

作者机构：Univ Dschang Dept Telecommun & Network Engn IUT Fotso Victor Bandjoun POB 134 Bandjoun Cameroon Univ Dschang Dept Elect Engn Res Unit Automat & Appl Comp IUT FV Bandjoun POB 134 Bandjoun Cameroon Ind Univ Ho Chi Minh City Fac Elect Technol Ho Chi Minh City Vietnam Univ Salento Dept Engn Innovat I-73100 Lecce Italy Univ Dschang Fac Sci Dept Phys Res Unit Condensed Matter Elect & Signal Proc POB 67 Dschang Cameroon 

出 版 物：《AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS》 (AEU Int. J. Electron. Commun.)

年 卷 期：2025年第191卷

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

主　　题：Fractional-order memristive Hopfield neural network Autapse memristor External electromagnetic radiation memristor Memristor initial boosting behaviors Microcontroller implementation 

摘      要：The Hopfield neuron is an artificial neuron model used for pattern memorization and recognition. It exhibits a complex dynamic with stable states corresponding to memorized patterns. In order to grasp a more complete representation of the information exchange between two neurons, emphasizing the importance of neuronal connections in brain processing, we propose in this work the coupling of two fractional-order Hopfield neurons via a fractional-order flux-controlled multistable memristor. Each of these two neurons incorporates a selfcoupling memristive component, called an autapse memristor. Additionally, the second neuron is subjected to an external electromagnetic radiation, simulated by an additional memristor. The I-V characteristics of the memristors integrated in this model are analyzed through numerical simulations. The simulations of model dynamics versus its diverse parameters have revealed rich and complex dynamical behaviors. These simulations demonstrate that the proposed model generates a variety of homogeneous and heterogeneous chaotic attractors, distributed at diverse locations. The elaborated memristor coupled fractional-order bi-Hopfield neuron MCFBHN model is implemented on an Arduino-Due platform. A comparison of the results of the two approaches shows good consistency.