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
相关论文
共 50 条
  • [31] Simulation Verification and Evaluation of Radar BIT System Based on Multi-Signal Model
    Xue, Jia
    Cai, Jinyan
    Zhang, Hong
    PROCEEDINGS OF 2009 8TH INTERNATIONAL CONFERENCE ON RELIABILITY, MAINTAINABILITY AND SAFETY, VOLS I AND II: HIGHLY RELIABLE, EASY TO MAINTAIN AND READY TO SUPPORT, 2009, : 879 - +
  • [32] A changing forgetting factor RLS for online identification of nonlinear systems based on ELM–Hammerstein model
    Yinggan Tang
    Zhenzhen Han
    Ying Wang
    Linlin Zhang
    Qiushen Lian
    Neural Computing and Applications, 2017, 28 : 813 - 827
  • [33] Research on Satellite Integrated Fault Diagnosis System based on Multi-Signal Model
    Hu Zheng
    Zhang Shigang
    Kong Lingkuan
    Xiang Rui
    PROCEEDINGS OF THE THIRD INTERNATIONAL SYMPOSIUM ON TEST AUTOMATION & INSTRUMENTATION, VOLS 1 - 4, 2010, : 1340 - 1345
  • [34] An imaging analysis protocol to trace, quantify, and model multi-signal neuron morphology
    Nanda, Sumit
    Bhattacharjee, Shatabdi
    Cox, Daniel N.
    Ascoli, Giorgio A.
    STAR PROTOCOLS, 2021, 2 (02):
  • [35] An Initial Implementation of Testability Analysis Based on Multi-Signal Flow Graph Model
    Lyu, Chao
    Ding, Hao
    Liu, Shanshan
    Wang, Lixin
    Wang, Shujuan
    PROCEEDINGS OF THE 2013 IEEE 8TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA), 2013, : 1874 - 1879
  • [36] ACOUSTIC SIGNAL ANALYSIS FOR NOISE SOURCE IDENTIFICATION IN MECHANISMS
    PETERSON, RH
    ZELENSKI, RE
    ACKERMAN, AD
    IBM JOURNAL OF RESEARCH AND DEVELOPMENT, 1972, 16 (03) : 249 - &
  • [37] MULTIVARIATE SIGNAL ANALYSIS FOR PROCESS NOISE SOURCE IDENTIFICATION
    BERGDAHL, BG
    ESPEFALT, R
    PROGRESS IN NUCLEAR ENERGY, 1982, 9 : 149 - 160
  • [38] Research on Testability Modeling with Bayesian Network Based on Multi-signal Flow Model
    Yang, Chunling
    Zhang, Siwen
    Tong, Chenmin
    Gu, Dazhong
    PROCEEDINGS OF THE 2013 IEEE 8TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA), 2013, : 1870 - 1873
  • [39] Identification of Stochastic Systems with Colored Noise by the Model Equivalence Theory
    Meng, Dandan
    Ding, Feng
    PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC), 2016, : 5646 - 5651
  • [40] Optimal Signal/Filter Design for Multi-Target Detection in Colored Noise
    Patton, Lee K.
    Rigling, Brian D.
    2011 IEEE RADAR CONFERENCE (RADAR), 2011, : 276 - 281
12345