Genetic Programming Modeling On Chaotic Time Series
Genke Yang · Dianzi xuebao · 2005
This paper proposes Genetic Programming Modeling (GPM) algorithm on chaotic time series.GP is used here to search for appropriate model structures in function space,and Particle Swarm Optimization (PSO) algorithm is introduced for Nonlinear Parameter Estimation (NPE) of dynamic model structures.In addition,GPM integrates the results from Nonlinear Time Series Analysis (NTSA) to adjust the parameters and as the criterion of founded models.The simulation shows the effectiveness of such improvements on modeling chaotic time series.