A self-error corrector integrating K-means clustering with Markov model for marine craft maneuvering prediction with experimental verification

Abstract

A novel approach for maneuvering prediction of marine crafts in data-limited situations is presented in this study. The approach is a hybrid strategy that combines least square-support vector machines (LS-SVM) and a self-error corrector that integrates K-means clustering with Markov model. The strategy allows testing errors caused by LS-SVM to be compensated effectively by the dedicated self-error corrector. To classify the inputs of the Markov model-based error corrector rapidly and properly, the K-means clustering algorithm is resorted to classifying the training errors obtained from LS-SVM. Assuming that maneuvering prediction for marine crafts with a limited number of simulation or experiment data, a comparative analysis of the prediction performance between the proposed strategy and the conventional method based on LS-SVM is performed. The results of numerical simulations and experiments demonstrate the superiority of the proposed strategy, which reduces the root mean square error of surge speed by up to 86.2% in simulation tests and 54.5% in experiments.