关键词: 基因表达式编程, 动态系统建模, 高阶常微分方程模型, 时间序列预测

Abstract: Aiming at the defects of dynamic system prediction model such as low modeling efficiency and lack of explicit models, a High Order Ordinary Differential Equations Model based on GEP（GEP-HODE） has been proposed in this passage. In the first place, higher order differentials are used to express the change characteristics of one-dimensional data and an explicit model is gotten by modeling the high-order differential data using GEP. Then, this paper reduces the order of HODE model and uses numerical methods to obtain the predicted values. This method utilizes the coding characteristics of the “genotype-phenotype” of the GEP, achieving synchronization of modeling and parameter optimization, and greatly improving modeling efficiency. Yearly mean sunspots number are used to model and predict. The results show that this method has more efficiency of model compared with GP hybrid method and more accuracy compared with other predict models like hybrid BP neural network model.

Key words: gene expression programming, dynamic system modeling, high order differential equation model, time series prediction