Ship information risk assessment based on combinatorial optimization algorithm
In order to avoid major ship navigation accidents caused by ship information risks,a ship information risk assessment method based on combinatorial optimization algorithms is studied.Selecting a total of 17 indicators from four as-pects of communication,environment,management,and human factors,a ship information risk assessment index system is constructed.It is used as input data for the radial basis function(RBF)neural network input layer,and after hidden layer mapping operation,the evaluated ship information risk level is output through the output layer.A combination optimization algorithm combining fuzzy C-means clustering algorithm and genetic algorithm is adopted,Reasonably selecting the center vector of the hidden layer in the RBF neural network and optimizing it to obtain the optimal width and weight vector of the hidden layer basis function,in order to improve the effectiveness of ship information risk assessment.The experimental res-ults show that this method can effectively evaluate the information risk of multiple ships,and based on the evaluation results,identify the factors that cause ship information risk and provide targeted guidance suggestions.
NETL
NSTL
万方数据
