Data filtering based multi-innovation extended gradient method for controlled autoregressive autoregressive moving average systems using the maximum likelihood principle
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- 作者：Feiyan Chena ; fychen12@126.com" class="auth_mail" title="E-mail the corresponding author ; Feng Dinga ; b ; fding@jiangnan.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Ahmed Alsaedib ; Tasawar Hayatb ; c
- 关键词：System identification ; Maximum likelihood ; Multi-innovation ; Recursive estimation ; Data filtering
- 刊名：Mathematics and Computers in Simulation
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：132
- 期：Complete
- 页码：53-67
- 全文大小：413 K
文摘
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The filtering technique and the multi-innovation identification theory are combined.
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A filtering based maximum likelihood multi-innovation gradient method is given.
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The proposed algorithm can improve the parameter estimation accuracy.
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The proposed method requires lower computational load because of lower dimensions.
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The proposed algorithm can be extended to study problems of other systems.
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