Title :
Neural network based combining prediction model and its application in ship motion prediction
Author :
Peng, Xiuyan ; Hu, Zhonghui ; Li, Tengfei
Author_Institution :
Coll. of Autom., Harbin Eng. Univ., Harbin, China
Abstract :
The single-step neural network combining prediction model and the multi-step neural network combining prediction model are researched on this paper, and those models are applied to the ship pitch motion prediction. Firstly the single-step neural network combination prediction model is proposed and the optimal combination of the conventional single-step prediction model attained by the neural network. And based on the single-step neural network combination prediction model, the multistep neural network prediction model is proposed, and we get the optimal combination of the conventional single-step prediction model by the neural network. Used the periodgram model and AR model combined by the two neural networks model, the ship pitch motion data are predicted. The simulation result shows that these two combining models are more accurate than the single prediction model and the multi-step combining model can get better results in phase tracking and the error of amplitude.
Keywords :
motion control; neurocontrollers; prediction theory; ships; AR model; amplitude error; conventional single-step prediction model; multistep combining model; multistep neural network combining prediction model; multistep neural network prediction model; neural network based combining prediction model; neural networks model; optimal combination; periodgram model; phase tracking; ship motion prediction; ship pitch motion data; ship pitch motion prediction; single prediction model; single-step neural network combining prediction model; Artificial neural networks; Automation; Data models; Kalman filters; Manganese; Marine vehicles; Predictive models; Neural networks; combining prediction model; ship pitch motion;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-6712-9
DOI :
10.1109/WCICA.2010.5554740