摘要
An important problem in engineering is the unknown parameters estimation in nonlinear systems.In this paper,a novel adaptive particle swarm optimization (APSO) method is proposed to solve this problem.This work considers two new aspects,namely an adaptive mutation mechanism and a dynamic inertia weight into the conventional particle swarm optimization (PSO) method.These mechanisms are employed to enhance global search ability and to increase accuracy.First,three well-known benchmark functions namely Griewank,Rosenbrock and Rastrigrin are utilized to test the ability of a search algorithm for identifying the global optimum.The performance of the proposed APSO is compared with advanced algorithms such as a nonlinearly decreasing weight PSO (NDWPSO) and a real-coded genetic algorithm (GA),in terms of parameter accuracy and convergence speed.It is confirmed that the proposed APSO is more successful than other aforementioned algorithms.Finally,the feasibility of this algorithm is demonstrated through estimating the parameters of two kinds of highly nonlinear systems as the case studies.
An important problem in engineering is the unknown parameters estimation in nonlinear systems.In this paper,a novel adaptive particle swarm optimization （APSO） method is proposed to solve this problem.This work considers two new aspects,namely an adaptive mutation mechanism and a dynamic inertia weight into the conventional particle swarm optimization （PSO） method.These mechanisms are employed to enhance global search ability and to increase accuracy.First,three well-known benchmark functions namely Griewank,Rosenbrock and Rastrigrin are utilized to test the ability of a search algorithm for identifying the global optimum.The performance of the proposed APSO is compared with advanced algorithms such as a nonlinearly decreasing weight PSO （NDWPSO） and a real-coded genetic algorithm （GA）,in terms of parameter accuracy and convergence speed.It is confirmed that the proposed APSO is more successful than other aforementioned algorithms.Finally,the feasibility of this algorithm is demonstrated through estimating the parameters of two kinds of highly nonlinear systems as the case studies.
出处
《自动化学报》
EI
CSCD
北大核心
2011年第5期541-549,共9页
Acta Automatica Sinica
关键词
自动化
PSO
APSO
线性系统
Particle swarm optimization （PSO）
parameter estimation
nonlinear dynamics
inertia weight
adaptive mutation
