船舶市场趋势预测的三阶段模型

Three-stage model of ship market trend prediction

为提高船舶市场趋势预测的精度,针对以往在神经网络应用时仅单纯改进隐层环节算法的局限性,综合模糊聚类方法、数据修正和插值算法,对输入环节的数据进行降维和增量处理,构建船舶市场趋势预测的三阶段模型.首先,利用模糊聚类方法对历史数据进行分类,降低数据非线性;然后,通过数据修正和插值算法,在不改变数据规律的情况下增加每类数据的数据量;最后,利用处理完毕的数据训练神经网络.实例结果证明,三阶段模型在船舶市场趋势预测方面是有效的.

In order to improve the prediction accuracy of ship market trend,in view of the limitation that the neural network is simply used to improve the algorithm in the hidden layer,the reduction of data di-mension and the increase of the data amount are carried out in the input layer by the fuzzy clustering method,data correction and interpolation algorithm,and a three-stage model of ship market trend predic-tion is constructed. First,the fuzzy clustering method is used to classify the historical data to reduce the nonlinearity of data. Then,through the data correction and interpolation algorithm,the amounts of vari-ous types of data increase without changing data regularity. Finally,the neural network is trained by the processed data. The application results show that the three-stage model is effective in the ship market trend prediction.