水上交通流冲突严重度BP神经网络评价方法

目前的水上交通流评价方法在评价指标关系模糊、来源不清等情况下难以运用,且主观性较强,存在评价结果严重偏离实际的情况,忽视了客观性不足的问题.为降低专家主观性对水上交通流冲突严重度评价的影响,基于BP神经网络建立评价模型,并通过网络训练进行函数比较,确定最符合模型设定要求的Trainlm函数,以及精度与迭代次数.由于数据的差异性会对BP神经网络的训练效率和评价精度造成影响,基于聚类分析与BP神经网络建立新的评价模型,将训练数据按照欧几里得度量进行归类开展神经网络训练,分别对水上交通流冲突严重度进行评价.运用9个水道数据为例对模型进行验证,通过比较聚类分析数据与未处理的原始数据在BP神经网络中的评价结果,发现评价结果平均误差从42.05%降低到23.74%,进一步验证了BP神经网络在该领域的可行性.评价模型利用聚类分析与BP神经网络相结合的方法,不仅客观性较强,而且与单一使用BP神经网络的模型相比提升了评价精度. 

Evaluation Methods of Severity Level of Water Traffic Flow Conflict Based on BP Neural Network

Existing approaches to evaluate ship traffic flow are difficult to use when the relationships among the indicators are not clear.These methods are subjective and the results are usually deviated from the real cases.In order to reduce effects of experts' subjectivity when evaluating the severity of marine traffic flow conflicts, a model based on BP neural network is proposed in this paper, in which the precision of Trainlm function is calculated and the number of iterations is determined using network training.In order to avoid the influences of the data on training efficiency and accuracy of BP neural network, a new model based on clustering analysis and BP neural network is proposed.The training data is classified according to Euclidean metric in order to carry out neural network training.This model is then used to evaluate the severity of the conflicts of water traffic flow.A case study of 9 channels is then performed to evaluate the proposed model.Comparing the processed data by clustering analysis with the original data, the evaluation error is reduced to 23.74% from 42.05%, which verifies the feasibility of BP neural network in this case.The modified model, which combining BP neural network with cluster analysis, has higher precision and more objectivity.