A Multi-Interval Homotopy Analysis Method Using Multi-Objective Optimization for Analytically Analyzing Chaotic Dynamics in Memristive Circuit

被引:4
|
作者
Hu, Wei [1 ]
Luo, Haibo [2 ]
Chen, Chuandong [1 ]
Wei, Rongshan [1 ]
机构
[1] Fuzhou Univ, Coll Phys & Informat Engn, Fuzhou 350108, Fujian, Peoples R China
[2] Minjiang Univ, Elect Informat & Control Fujian Univ Engn Res Ctr, Fuzhou 350121, Fujian, Peoples R China
基金
中国国家自然科学基金;
关键词
Analytical method; chaotic dynamics; multi-interval homotopy analysis method; multi-objective optimization; memristive circuit; PARTICLE SWARM OPTIMIZATION; NEURAL-NETWORKS; TOPSIS METHOD; PERTURBATION METHOD; GENETIC ALGORITHM; SYNCHRONIZATION; DELAYS; PARAMETER; EQUATION; SYSTEMS;
D O I
10.1109/ACCESS.2019.2936014
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Memristive nonlinear system has drawn much attention in recent years, due to its rich and complex dynamical characteristics. However, there are few studies focus on the analytical analysis of this significant system. In this paper, a novel analytical method for analyzing the chaotic trajectories of memristive circuit is proposed. This method combines Homotopy Analysis Method (HAM) and Multi-objective Optimization (MO), i.e., the convergence control parameter of traditional HAM is divided into lots of subintervals in the time domain and respectively optimized by MO, for accurately solving the Ordinary Differential Equations describing memristive circuits. Hence, this method is named by MO-based multi-interval HAM (MO-MIHAM). By using MO-MIHAM, we accurately tracked the chaotic trajectories of the classical Memristor-Capacitor-Inductor (MCL) circuit and current memristive Band Pass Filter (BPF) chaotic circuit. Furthermore, based on the comparisons of errors between analytical approximate solutions derived from MO-MIHAM and solutions solved by traditional homotopy-based analytical methods and by Runge-Kutta-Fehlberg Method (RKF45) based numerical analysis, we found that, MO-MIHAM is characterized by higher approximation accuracy and computational performance (comprehensively considering the accuracy, computational complexity and execution time by a proposed Quality Factor) among the homotopy-based analytical methods, due to the optimized convergence control parameters in subintervals. Besides this major advantage, MO-MIHAM enables both qualitative and quantitative analyses and high freedom to choose homotopy-related terms for simplicity, and it is insensitive to convergence issues. Therefore, it is a powerful tool for exploratory studies for analytically analyzing chaotic dynamics in memristive circuit.
引用
收藏
页码:116328 / 116341
页数:14
相关论文
共 50 条
  • [21] Dynamics analysis and multi-objective optimization for a dry friction damper
    Huang, Zhonghe
    Liu, Chuliang
    Sun, Qiao
    INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES, 2025, 287
  • [22] Integrated Circuit Optimization by Means of Evolutionary Multi-Objective Optimization
    Blesken, Matthias
    Chebil, Anouar
    Rueckert, Ulrich
    Esquivel, Xavier
    Schuetze, Oliver
    GECCO-2011: PROCEEDINGS OF THE 13TH ANNUAL GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2011, : 807 - 812
  • [23] Enhanced Multi-Objective Energy Optimization by a Signaling Method
    Soares, Joao
    Borges, Nuno
    Vale, Zita
    Oliveira, P. B. de Moura
    ENERGIES, 2016, 9 (10):
  • [24] Compensation method in genetic algorithm for multi-objective optimization
    Yuan Hua
    Chen Guo-qing
    PROCEEDINGS OF 2005 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1 AND 2, 2005, : 943 - 946
  • [25] Deep Learning Test Optimization Method Using Multi-objective Optimization
    Mu Y.-Z.
    Wang Z.
    Chen X.
    Chen J.-J.
    Zhao J.-K.
    Wang J.-M.
    Ruan Jian Xue Bao/Journal of Software, 2022, 33 (07): : 2499 - 2524
  • [26] Evolutionary algorithms with preference polyhedron for interval multi-objective optimization problems
    Gong, Dunwei
    Sun, Jing
    Ji, Xinfang
    INFORMATION SCIENCES, 2013, 233 : 141 - 161
  • [27] A quantum multi-objective optimization algorithm based on harmony search method
    Sadeghi Hesar, Alireza
    Kamel, Seyed Reza
    Houshmand, Mahboobeh
    SOFT COMPUTING, 2021, 25 (14) : 9427 - 9439
  • [28] Multi-objective structural optimization of vehicle wheels: a method for preliminary design
    Stabile, P.
    Ballo, F.
    Gobbi, M.
    Previati, G.
    OPTIMIZATION AND ENGINEERING, 2024, 25 (02) : 605 - +
  • [29] Multi-Objective Optimization of Hybrid Renewable Energy System Using an Enhanced Multi-Objective Evolutionary Algorithm
    Ming, Mengjun
    Wang, Rui
    Zha, Yabing
    Zhang, Tao
    ENERGIES, 2017, 10 (05)
  • [30] MILP method for objective reduction in multi-objective optimization
    Vazquez, Daniel
    Fernandez-Torres, Maria J.
    Ruiz-Femenia, Ruben
    Jimenez, Laureano
    Caballero, Jose A.
    COMPUTERS & CHEMICAL ENGINEERING, 2018, 108 : 382 - 394