控制理论与应用2025，Vol.42Issue(10)：1981-1989,9.DOI:10.7641/CTA.2024.30616
用于模型结构和参数联合辨识的共轭梯度追踪辨识方法
Conjugate gradient pursuit identification algorithm for combined model structure and parameter identification
摘要
Abstract
It is a significant challenge to simultaneously identify the model structure and parameters with limited sam-pling data.By combining the conjugate gradient optimization algorithm and a greedy algorithm,a conjugate gradient pursuit based sparse identification method is proposed.Firstly,the system model is transformed into a sparse parameter identification model.Then,the greedy search and conjugate gradient optimization are used to select and estimate the posi-tions and values of non-zero parameters,system orders and delays are then obtained based on the sparse parameter structure.Simulation examples show that this method can utilize limited sampling data to simultaneously identify the structure and parameters of the system,with the advantages of fast iteration speed and low computational complexity.Compared with existing greedy identification methods,it has lower computational complexity than the orthogonal matching pursuit based method and fewer iterations than the gradient pursuit based method,while achieving a higher model accuracy than the gradient pursuit based method.关键词
参数辨识/结构辨识/共轭梯度/共轭梯度追踪Key words
parameter identification/structure identification/conjugate gradient/conjugate gradient pursuit引用本文复制引用
刘艳君,刘维维,陈晶,丁锋..用于模型结构和参数联合辨识的共轭梯度追踪辨识方法[J].控制理论与应用,2025,42(10):1981-1989,9.基金项目
国家自然科学基金项目(62373165),江苏省自然科学基金项目(BK20201339),中国博士后科学基金项目(2022M711361)资助.Supported by the National Natural Science Foundation of China(62373165),the Natural Science Foundation of Jiangsu Province(BK20201339)and the China Postdoctoral Science Foundation(2022M711361). （62373165）
