Title :
Research for economy shipping of oceangoing vessel based on the FOA-SVR
Author :
Shi Bu-hai ; Guo Xie-tao ; Zhang Ben
Author_Institution :
Sch. of Autom. Sci. & Eng., South China Univ. of Technol., Guangzhou, China
Abstract :
In this paper, aiming at the non-linear characteristics of ocean-going sailing vessel model and the deficiencies of either the traditional mathematical methods or the neural networking modeling, an improved support vector regression (SVR) economical sailing predictive modeling method based on the fly fruit optimization algorithm (FOA) is proposed. First, using data mining techniques on the ship sailed data mining, screening, then use the processed data to establish economic sailing forecast model of the ocean-going vessels. The model synthetically considers the external natural variables which influence on the ocean-going sailing vessel and provides the economical way of sailing under the multi-variable natural weather conditions. The simulation results show that this model is able to accurately predict the pitch for the economical sailing control according to the external multivariable. It has been certificated that this method is an effective novel means to study the economical sailing control of the ocean-going vessel.
Keywords :
control nonlinearities; data mining; economics; learning systems; multivariable control systems; neurocontrollers; optimisation; predictive control; regression analysis; ships; support vector machines; FOA-SVR; SVR economical sailing predictive modeling method; data mining technique; economic sailing forecast model; economical sailing control; economy shipping; external multivariable; external natural variables; fly fruit optimization algorithm; mathematical method; multivariable natural weather conditions; neural networking modeling; nonlinear characteristics; ocean-going sailing vessel model; pitch prediction; ship sailed data mining; support vector regression; Economics; Fuels; Marine vehicles; Prediction algorithms; Predictive models; Support vector machines; Training; FOA; SVR; economical speed; oceangoing voyage;
Conference_Titel :
Control Conference (CCC), 2013 32nd Chinese
Conference_Location :
Xi´an