Ship motion extreme short time prediction of ship pitch based on diagonal recurrent neural network  

作　　者：SHEN Yan XIE Mei-ping 

机构地区：[1]School of Science, Harbin Engineering University, Harbin 150001, China [2]School of Information Management and Engineering, Shanghai University of Finanee and Economics, Shanghai 200433, China

出　　处：《Journal of Marine Science and Application》2005年第2期56-60,共5页船舶与海洋工程学报（英文版）

摘　　要：A DRNN (diagonal recurrent neural network) and its RPE (recurrent prediction error) learning algorithm are proposed in this paper .Using of the simple structure of DRNN can reduce the capacity of calculation. The principle of RPE learning algorithm is to adjust weights along the direction of Gauss-Newton. Meanwhile, it is unnecessary to calculate the second local derivative and the inverse matrixes, whose unbiasedness is proved. With application to the extremely short time prediction of large ship pitch, satisfactory results are obtained. Prediction effect of this algorithm is compared with that of auto-regression and periodical diagram method, and comparison results show that the proposed algorithm is feasible.

关 键 词：extreme short time prediction diagonal recursive neural network recurrent prediction error learning algorithm UNBIASEDNESS 

分 类 号：U661.4[交通运输工程—船舶及航道工程]