化工学报2018，Vol.69Issue(3)：1129-1135,7.DOI:10.11949/j.issn.0438-1157.20171518
基于近似偏最小一乘准则的多变量非线性系统辨识方法
Partial approximate least absolute deviation for multivariable nonlinear system identification
摘要
Abstract
Based on approximate least absolute deviation and principal component analysis, the partial approximate least absolute deviation for non-linear system identification is carried out aiming at multivariable Hammerstein model with linear correlation of input signals. An approximate least absolute deviation objective function is established by introducing a deterministic function to replace the absolute value under certain situations. The proposed method can overcome the disadvantage of large square residual of least square criterion when the identification data is disturbed by the impulse noise which obeys symmetrical alpha stable(SαS)distribution.By adopting principal component analysis to eliminate the linear correlation among the elements of data vector of nonlinear systems, the unique solution of model parameters can be easily acquired by the proposed method. The simulation shows that the proposed method has stronger robustness than the partial least square (PLS) method in the identification of multivariable Hammerstein model with white noise and impulse noise under the above situation.关键词
参数辨识/主成分分析/多变量Hammerstein模型/偏最小一乘/动态仿真/尖峰噪声Key words
parameter identification/principal component analysis/multivariable Hammerstein model/partial least absolute deviation/dynamic simulation/impulse noise分类
信息技术与安全科学引用本文复制引用
徐宝昌,张华,王学敏..基于近似偏最小一乘准则的多变量非线性系统辨识方法[J].化工学报,2018,69(3):1129-1135,7.基金项目
国家重点研发计划项目(2016YFC0303700).supported by the National Key Research and Development Program of China(2016YFC0303700). （2016YFC0303700）
