Multi-signal Source Identification of ELM Hammerstein Model with Colored Noise

被引：0
|
作者：
Han, Zhenzhen [1 ,2 ]
Cheng, Bin [1 ,2 ]
Wang, Yunli [1 ,2 ]
Shao, Yunxia [1 ,2 ]
机构：
[1] Hebei Acad Sci, Inst Appl Math, Shijiazhuang 050081, Hebei, Peoples R China
[2] Hebei Authenticat Technol Engn Res Ctr, Shijiazhuang, Hebei, Peoples R China
来源：
2018 IEEE 3RD INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYSIS (ICCCBDA) | 2018年
关键词：
hammerstein model; extreme learning machine; Multi-signal source; recursive extended least squares algorithm; identification;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Hammerstein model is used in practical industrial process widely. A novel ELM-Hammerstein model with colored noise is proposed, where the extreme learning machine (ELM) is used to describe static nonlinear part and the dynamic linear part is described by CARAR model. The purpose is to identify the parameters of ELM-Hammerstein model. But, intermediate signal can not measure directly in the process of identification. So special signal is employed to separate the static nonlinear part and the dynamic linear part of the Hammerstein model. Further, recursive extended least squares (RELS) algorithm is applied to compute the unknown parameters of linear part. As a result, the proposed method can describe the nonlinear system with colored noised with high accuracy. Simulation example demonstrates its effectiveness.
引用
收藏
页码：457 / 461
页数：5
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