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 proceedingConference article published in proceeding or bookAcademic researchpeer-review

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 languageEnglish
Title of host publication3rd International Symposium on Intelligent Information Technology Application Workshops, IITAW 2009
Pages460-463
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2009
Externally publishedYes
Event3rd International Symposium on Intelligent Information Technology Application Workshops, IITAW 2009 - NanChang, China
Duration: 21 Nov 200922 Nov 2009

Conference

Conference3rd International Symposium on Intelligent Information Technology Application Workshops, IITAW 2009
Country/TerritoryChina
CityNanChang
Period21/11/0922/11/09

Keywords

  • Generalized heave motion
  • Wave compensating Elman neural network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

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