摘要
基于免疫细胞亚群划分理论和生命周期理论,提出一种用于多项式非线性自回归滑动平均(NARMAX)模型辨识的多目标免疫GEP算法,并重新定义了适合于NARMAX模型辨识的细胞创建算子和基因操作算子.该算法基于多目标优化的最优解通常为一个集合的特点,通过各亚群的最优解集在总最优解集中的变化来判断各亚群所对应参数的优劣,进而确定下一步的搜索方向.仿真结果表明,多目标免疫GEP算法可以同时正确地辨识出非线性系统的结构和参数.
Based on the immune cell subsets division and immune life theory, an immune based multiobjective gene expression programming（GEP） algorithm for identifying the polynomial nonlinear auto regressive moving average with exogenous inputs（NARMAX） model is proposed. In the algorithm, the cell initialization method and gene operators are newly defined. Based on the multi-optimal-solutions characteristic of multiobjective optimization problems, the parameter performance of each subset is estimated by the optimal solutions of the subset in the total optimal solutions, and then the search direction for the next generation is determined. The simulation results show that the proposed algorithm performs well in the polynomial NARMAX modeling, and the structure and the parameters of the nonlinear system can be identified at the same time.
出处
《控制与决策》
EI
CSCD
北大核心
2014年第6期1009-1015,共7页
Control and Decision
基金
国家自然科学基金项目(51306082)
关键词
多目标
免疫
基因表达式编程
非线性自回归滑动平均模型
multiobjective
immune
gene expression programming（GEP）
nonlinear auto regressive moving average with exogenous inputs model
