A recursive least squares parameter estimation algorithm for output nonlinear autoregressive systems using the input-output data filtering

作者：Ding, Feng^*; Wang, Yanjiao; Dai, Jiyang; Li, Qishen; Chen, Qijia

来源：Journal of the Franklin Institute, 2017, 354(15): 6938-6955.

DOI：10.1016/j.jfranklin.2017.08.009

• IDENTIFICATION ALGORITHM
• NEWTON ITERATION
• HAMMERSTEIN SYSTEMS
• DYNAMICAL-SYSTEMS
• AUXILIARY MODEL
• CARMA SYSTEMS
• CONVERGENCE
• DELAY
• STATE

) Nonlinear systems exist widely in industrial processes. This paper studies the parameter estimation methods of establishing the mathematical models for a class of output nonlinear systems, whose output is nonlinear about the past outputs and linear about the inputs. We use an estimated noise transfer function to filter the input-output data and obtain two identification models, one containing the parameters of the system model, and the other containing the parameters of the noise model. Based on the data filtering technique, a data filtering based recursive least squares algorithm is proposed. The simulation results show that the proposed algorithm can generate more accurate parameter estimates than the recursive generalized least squares algorithm.

• 出版日期2017-10
• 单位江南大学; 青岛科技大学; 南昌航空大学