摘要
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.
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.
出处
《信息与控制》
CSCD
北大核心
2007年第5期592-596,603,共6页
Information and Control
关键词
基因表达式编程
遗传算法
非线性系统
系统辨识
多目标优化
NARMAX模型
gene expression programming（GEP）
genetic algorithm（GA）
nonlinear system
system identification
multi-objective optimization
NARMAX model
