Online Ship Rolling Prediction Using an Improved OS-ELM

摘要：

　　In this paper, an improved online sequential extreme learning machine (OS-ELM) is applied on ship roll motion prediction.The OS-ELM is improved by temporal difference(TD)learning which is one of the mostly conventionally used prediction methods in reinforcement learning problem; the model dimension is also optimized by Akaike information criterion(AIC).Online sequential extreme learning machine is an efficient algorithm for on-line construction of single-hidden-layer feedforward networks(SLFNs).Ships roll motion is hard to be predicted because it is a complex process influenced by various time-varying navigational status and environmental factors.The improved OS-ELM was applied to the simulation of online ship roll motion prediction.Results demonstrate that the proposed method can online give predictions for ship roll motion with extreme fast speed and considerable high accuracy.

关键词： Ship rolling motion OS-ELM Online prediction TD learning Akaike Information Criterion

作者: YU Chao YIN Jianchuan HU Jiangqiang ZHANG Anran

作者单位: Navigation College,Dalian Maritime University,Dalian 116026,P.R.China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 5043-5048

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）