Lilian Huang, Fangyi Chen, Feiyi Geng, Lei Zheng, Xihong Yu
{"title":"记忆记忆Hopfield神经网络中网格多涡吸引子的生成与控制","authors":"Lilian Huang, Fangyi Chen, Feiyi Geng, Lei Zheng, Xihong Yu","doi":"10.1016/j.chaos.2025.116717","DOIUrl":null,"url":null,"abstract":"<div><div>The grid multi-vortex attractors previously generated have typically been derived by integrating the multi-piecewise nonlinear magnetron memristor model into neural network frameworks, with limited exploration of alternative operational mechanisms. To solve this problem, a method for constructing meshed multi-vortex attractors using a memristor-based Hopfield neural network is introduced by this paper. Firstly, a trineuron-based memristor Hopfield neural network is proposed, which can generate and regulate multi-vortex attractors. At the same time, the influence of multilayer logic pulses on the dynamics of the memristor-based Hopfield neural network is focused on by this paper, and it discusses how different pulse modes regulate the attractor state of the network, thereby revealing the profound influence of pulse regulation on network behavior. In addition, the migration control behavior of the multi-vortex attractor and the grid multi-vortex attractor is studied from multiple dimensions. Ultimately, by developing an analog circuit, the numerical simulation results of the MHNN with multi-level logic pulses were replicated. The simulation results indicate the feasibility of implementing the method based on hardware.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"199 ","pages":"Article 116717"},"PeriodicalIF":5.3000,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Generation and control of grid multi-vortex attractors in memristive Hopfield neural network\",\"authors\":\"Lilian Huang, Fangyi Chen, Feiyi Geng, Lei Zheng, Xihong Yu\",\"doi\":\"10.1016/j.chaos.2025.116717\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The grid multi-vortex attractors previously generated have typically been derived by integrating the multi-piecewise nonlinear magnetron memristor model into neural network frameworks, with limited exploration of alternative operational mechanisms. To solve this problem, a method for constructing meshed multi-vortex attractors using a memristor-based Hopfield neural network is introduced by this paper. Firstly, a trineuron-based memristor Hopfield neural network is proposed, which can generate and regulate multi-vortex attractors. At the same time, the influence of multilayer logic pulses on the dynamics of the memristor-based Hopfield neural network is focused on by this paper, and it discusses how different pulse modes regulate the attractor state of the network, thereby revealing the profound influence of pulse regulation on network behavior. In addition, the migration control behavior of the multi-vortex attractor and the grid multi-vortex attractor is studied from multiple dimensions. Ultimately, by developing an analog circuit, the numerical simulation results of the MHNN with multi-level logic pulses were replicated. The simulation results indicate the feasibility of implementing the method based on hardware.</div></div>\",\"PeriodicalId\":9764,\"journal\":{\"name\":\"Chaos Solitons & Fractals\",\"volume\":\"199 \",\"pages\":\"Article 116717\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2025-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chaos Solitons & Fractals\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0960077925007301\",\"RegionNum\":1,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chaos Solitons & Fractals","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0960077925007301","RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Generation and control of grid multi-vortex attractors in memristive Hopfield neural network
The grid multi-vortex attractors previously generated have typically been derived by integrating the multi-piecewise nonlinear magnetron memristor model into neural network frameworks, with limited exploration of alternative operational mechanisms. To solve this problem, a method for constructing meshed multi-vortex attractors using a memristor-based Hopfield neural network is introduced by this paper. Firstly, a trineuron-based memristor Hopfield neural network is proposed, which can generate and regulate multi-vortex attractors. At the same time, the influence of multilayer logic pulses on the dynamics of the memristor-based Hopfield neural network is focused on by this paper, and it discusses how different pulse modes regulate the attractor state of the network, thereby revealing the profound influence of pulse regulation on network behavior. In addition, the migration control behavior of the multi-vortex attractor and the grid multi-vortex attractor is studied from multiple dimensions. Ultimately, by developing an analog circuit, the numerical simulation results of the MHNN with multi-level logic pulses were replicated. The simulation results indicate the feasibility of implementing the method based on hardware.
期刊介绍:
Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.