Fitting Nonlinear Signal Models Using the Increasing-Data Criterion

被引:36
作者
Li, Jimei [1 ]
Ding, Feng [1 ]
机构
[1] Jiangnan Univ, Sch Internet Things Engn, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Signal processing algorithms; Estimation; Parameter estimation; Approximation algorithms; Stability criteria; Prediction algorithms; Noise measurement; Function approximation; nonlinear signal; para- meter estimation; recursive search; hierarchical identification; APPROXIMATION; ALGORITHM;
D O I
10.1109/LSP.2022.3177352
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
To extract important information about the nonlinear signals, this letter makes the utmost of the fitting advantages of Gaussian and polynomial functions, and proposes a nonlinear signal model with broader applications. Then we focus on the parameter estimation issues of the proposed models in the presence of noises. The stability factor recursive algorithm is devised based on the increasing noisy data, which makes full use of the information from the nonlinear signals. Applying the hierarchical identification principle, a two-stage recursive algorithm with higher computational efficiency is developed for the nonlinear signals. The simulation results test the effectiveness of the proposed algorithms from the aspects of estimation accuracy and prediction effect.
引用
收藏
页码:1302 / 1306
页数:5
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