东南大学学报（英文版）2018，Vol.34Issue(1)：6-14,9.DOI:10.3969/j.issn.1003-7985.2018.01.002
基于改进粒子群算法的分数阶系统参数辨识
Parameter identification of the fractional-order systems based on a modified PSO algorithm
摘要
Abstract
In order to better identify the parameters of the fractional-order system, a modified particle swarm optimization (MPSO) algorithm based on an improved Tent mapping is proposed. The MPSO algorithm is validated with eight classical test functions, and compared with the POS algorithm with adaptive time varying accelerators (ACPSO), the genetic algorithm(GA), and the improved PSO algorithm with passive congregation(IPSO). Based on the systems with known model structures and unknown model structures, the proposed algorithm is adopted to identify two typical fractional-order models. The results of parameter identification show that the application of average value of position information is beneficial to making full use of the information exchange among individuals and speeds up the global searching speed. By introducing the uniformity and ergodicity of Tent mapping, the MPSO avoids the extreme value of position information, so as not to fall into the local optimal value. In brief, the MPSO algorithm is an effective and useful method with a fast convergence rate and high accuracy.关键词
粒子群优化/Tent映射/参数辨识/分数阶系统/被动聚集Key words
particle swarm optimization/Tent mapping/parameter identification/fractional-order systems/passive congregation分类
信息技术与安全科学引用本文复制引用
刘璐,单梁,蒋超,戴跃伟,刘成林,戚志东..基于改进粒子群算法的分数阶系统参数辨识[J].东南大学学报（英文版）,2018,34(1):6-14,9.基金项目
The National Natural Science Foundation of China(No.61374153,61473138,61374133),the Natural Science Foundation of Jiangsu Province(No.BK20151130),Six Talent Peaks Project in Jiangsu Province(No.2015-DZXX-011),China Scholarship Council Fund(No.201606845005). （No.61374153,61473138,61374133）
