公开(公告)号：US12093616B1

公开(公告)日：2024-09-17

申请号：US18585089

申请日：2024-02-23

Applicant: HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Xianbo Xiang ,  Chaicheng Jiang ,  Gong Xiang ,  Shaolong Yang ,  Qin Zhang

IPC: G06F30/27 ,  B63B39/00 ,  B63B71/00 ,  G06N3/09 ,  B63B43/04

CPC classification number: G06F30/27 ,  B63B39/00 ,  B63B71/00 ,  G06N3/09 ,  B63B43/04

Abstract: A method and a system for ship stability prediction by weighted fusion of RBFNN and random forest based on GD are provided. Firstly, input characteristics when predicting failure probabilities under different failure modes are determined through prior knowledge. Secondly, a mean square error of k-fold cross-validation is used as performance evaluation criterion of the RBFNN and the RF to search for model capacities of the RBFNN and the RF. Then, network parameters of the RBFNN are updated. Multiple random sample sets are generated using a bootstrap sampling method and are parallelly trained to generate multiple regression trees. A Gini index is used as an attribute division index, and a prediction result of the random forest is obtained. Finally, weight coefficients are introduced for weighted fusion of prediction results of the RBFNN and the RF. The weight coefficient is obtained by solving through iterative optimization of the gradient descent.