Multiple mixed state variable incremental integration for reconstructing extreme multistability in a novel memristive hyperchaotic jerk system with multiple cubic nonlinearity

作     者：王梦蛟 辜玲芳 Meng-Jiao Wang;Lingfang Gu

作者机构：School of Automation and Electronic InformationXiangtan UniversityXiangtan 411105China 

出 版 物：《Chinese Physics B》 (中国物理B（英文版）)

年 卷 期：2024年第33卷第2期

页      面：238-246页

核心收录：

学科分类：080903[工学-微电子学与固体电子学] 07[理学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 070201[理学-理论物理] 0702[理学-物理学] 

基　　金：Project supported by the National Natural Science Foundation of China(Grant No.62071411) the Research Foundation of Education Department of Hunan Province,China(Grant No.20B567). 

主　　题：extreme multistability hyperchaotic jerk system nonlinearity 

摘      要：Memristor-based chaotic systems with infinite equilibria are interesting because they generate extreme multistability.Their initial state-dependent dynamics can be explained in a reduced-dimension model by converting the incremental integration of the state variables into system parameters.However,this approach cannot solve memristive systems in the presence of nonlinear terms other than the memristor term.In addition,the converted state variables may suffer from a degree of divergence.To allow simpler mechanistic analysis and physical implementation of extreme multistability phenomena,this paper uses a multiple mixed state variable incremental integration(MMSVII)method,which successfully reconstructs a four-dimensional hyperchaotic jerk system with multiple cubic nonlinearities except for the memristor term in a three-dimensional model using a clever linear state variable mapping that eliminates the divergence of the state variables.Finally,the simulation circuit of the reduced-dimension system is constructed using Multisim simulation software and the simulation results are consistent with the MATLAB numerical simulation results.The results show that the method of MMSVII proposed in this paper is useful for analyzing extreme multistable systems with multiple higher-order nonlinear terms.