Conjugate gradient pursuit identification algorithm for combined model structure and parameter identification
LIU Yan-jun
LIU Wei-wei
CHEN Jing
DING Feng
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.
Keywords：parameter identificationstructure identificationconjugate gradientconjugate gradient pursuit
Publication Date：2025-10-30
Online Publishing Date：2025-11-13(First online date of this platform, not the publication date of the document)
Pages：9( 1981-1989 )