Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations  

作　　者：Xiangquan Li Zhengguang Xu Cheng Han Ning Li 

机构地区：[1]School of Automation and Electrical Engineering,University of Science and Technology Beijing,Key Laboratory of Knowledge Automation for Industrial Processes,Ministry of Education,Beijing,100083,China [2]School of Information Engineering,Jingdezhen University,Jingdezhen,333000,China

出　　处：《Computer Modeling in Engineering & Sciences》2022年第3期1807-1825,共19页工程与科学中的计算机建模（英文）

基　　金：This work was supported by the National Natural Science Foundation of China(62076025).

摘　　要：This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized observations.It proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system.The system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its convergence.Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.

关 键 词：Pattern moving multi-threshold quantized observations output error model auxiliary model parameter identification 

分 类 号：O17[理学—数学]