Modified genetic optimization-based locally weighted learning identification modeling of ship maneuvering with full scale trial

This paper explores a novel nonparametric identification modeling technique for ship maneuvering system. In order to solve the over-learning or under-learning problem which may exist in distance metric optimization process in the locally weighted learning (LWL), a modified genetic algorithm (GA) is proposed. In our algorithm, a novel fitness function is defined, which can assign the maximum fitness to the optimal distance metric. And the performance analysis is developed based on the schema theory. Additionally, GA is a global search algorithm that the locally optimal is avoided. The LWL is applied to identify the characteristics of ship maneuvering motion by using the optimal distance metric. The proposed scheme improves the nonlinear mapping ability of LWL especially in the highly nonlinear area. The illustrative examples are utilized to demonstrate the effectiveness of the proposed scheme, including a synthetic data set and the YUKUN scientific research vessel of Dalian Maritime University. The simulation results indicate that the proposed scheme is a powerful modeling tool for ship maneuvering system. (C) 2018 Published by Elsevier B.V.