基于GEP和GA技术的非线性系统辨识研究

给出了利用基因表达式编程（GEP）进行非线性系统辨识的方法,弥补了传统辨识方法需要过多预知信息的不足,有着比遗传编程（GP）更简洁有效的系统模型结构表达方式.利用改进的遗传算法（GA）并行地进行模型参数进化,可以在有限的给定数据内得到合适的模型.关于模型适应度的定义,综合考虑了精确性和复杂性因素,能够获取一种比较折中的辨识结果.仿真结果表明,这种方式可以快速、准确地获取非线性模型.

The Study of Nonlinear System Identification Based on GEP and GA Technique

A method based on gene expression programming(GEP) for identifying the nonlinear system model is presented,which makes up the insufficiency that traditional identification methods need much a priori information,and has a tidier and more efficient system model expression mode than genetic programming(GP).It uses the improved genetic algorithm(GA) to carry out the model parameter evolution in a parallel mode,and the appropriate models can be obtained with limited given data.The definition of model fitness considers fully the accuracy and complicacy factors,and can get a trade-off identification solution.The simulation result indicates that the presented method can obtain nonlinear model in a quick and accurate way.