Multi-step predictions for generalized heave motion of wave compensating platform based on ELMAN neural network

Zhigang Zeng; Guohua Chen

doi:10.1109/IITAW.2009.109

Multi-step predictions for generalized heave motion of wave compensating platform based on ELMAN neural network

Zhigang Zeng, Guohua Chen

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

BP algorithm is the typical supervised learning algorithm, so neural network cannot be trained on-line by it. For this reason, a new algorithm (TD-DBP), which was composed of temporal difference (TD) method and dynamic BP algorithm (DBP), was proposed to overcome the restriction. TD-DBP algorithm can make Elman network train on-line incrementally. The gradient descent momentum and adaptive learning rate TD-DBP algorithm can improve the training speed and stability of Elman network effectively. Using the collected real time data, the modified TD-DBP algorithm was able to realize direct multi-step predictions for generalized heave motion of wave compensating platform.

Original language	English
Title of host publication	3rd International Symposium on Intelligent Information Technology Application Workshops, IITAW 2009
Pages	460-463
Number of pages	4
DOIs	https://doi.org/10.1109/IITAW.2009.109
Publication status	Published - 1 Dec 2009
Externally published	Yes
Event	3rd International Symposium on Intelligent Information Technology Application Workshops, IITAW 2009 - NanChang, China Duration: 21 Nov 2009 → 22 Nov 2009

Conference

Conference	3rd International Symposium on Intelligent Information Technology Application Workshops, IITAW 2009
Country/Territory	China
City	NanChang
Period	21/11/09 → 22/11/09

Keywords

Generalized heave motion
Wave compensating Elman neural network

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications

More information

10.1109/IITAW.2009.109

Cite this

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title = "Multi-step predictions for generalized heave motion of wave compensating platform based on ELMAN neural network",

abstract = "BP algorithm is the typical supervised learning algorithm, so neural network cannot be trained on-line by it. For this reason, a new algorithm (TD-DBP), which was composed of temporal difference (TD) method and dynamic BP algorithm (DBP), was proposed to overcome the restriction. TD-DBP algorithm can make Elman network train on-line incrementally. The gradient descent momentum and adaptive learning rate TD-DBP algorithm can improve the training speed and stability of Elman network effectively. Using the collected real time data, the modified TD-DBP algorithm was able to realize direct multi-step predictions for generalized heave motion of wave compensating platform.",

keywords = "Generalized heave motion, Wave compensating Elman neural network",

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doi = "10.1109/IITAW.2009.109",

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Zeng, Z & Chen, G 2009, Multi-step predictions for generalized heave motion of wave compensating platform based on ELMAN neural network. in 3rd International Symposium on Intelligent Information Technology Application Workshops, IITAW 2009., 5419582, pp. 460-463, 3rd International Symposium on Intelligent Information Technology Application Workshops, IITAW 2009, NanChang, China, 21/11/09. https://doi.org/10.1109/IITAW.2009.109

Multi-step predictions for generalized heave motion of wave compensating platform based on ELMAN neural network. / Zeng, Zhigang; Chen, Guohua.
3rd International Symposium on Intelligent Information Technology Application Workshops, IITAW 2009. 2009. p. 460-463 5419582.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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N2 - BP algorithm is the typical supervised learning algorithm, so neural network cannot be trained on-line by it. For this reason, a new algorithm (TD-DBP), which was composed of temporal difference (TD) method and dynamic BP algorithm (DBP), was proposed to overcome the restriction. TD-DBP algorithm can make Elman network train on-line incrementally. The gradient descent momentum and adaptive learning rate TD-DBP algorithm can improve the training speed and stability of Elman network effectively. Using the collected real time data, the modified TD-DBP algorithm was able to realize direct multi-step predictions for generalized heave motion of wave compensating platform.

AB - BP algorithm is the typical supervised learning algorithm, so neural network cannot be trained on-line by it. For this reason, a new algorithm (TD-DBP), which was composed of temporal difference (TD) method and dynamic BP algorithm (DBP), was proposed to overcome the restriction. TD-DBP algorithm can make Elman network train on-line incrementally. The gradient descent momentum and adaptive learning rate TD-DBP algorithm can improve the training speed and stability of Elman network effectively. Using the collected real time data, the modified TD-DBP algorithm was able to realize direct multi-step predictions for generalized heave motion of wave compensating platform.

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Multi-step predictions for generalized heave motion of wave compensating platform based on ELMAN neural network

Abstract

Conference

Keywords

ASJC Scopus subject areas

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