文献类型： 外文期刊

作者： Zhang, Yizhuo ¹ ;

作者机构： 1.Tokyo Univ Marine Sci & Technol, Tokyo, Japan

2.Chinese Acad Fishery Sci, Beijing, Peoples R China

关键词： Export scale; Neural network; Electronic commerce; Predictive model

期刊名称：COGNITIVE SYSTEMS RESEARCH （ 影响因子：3.523； 五年影响因子：3.134 ）

ISSN： 1389-0417

年卷期： 2019 年 57 卷

页码：

收录情况： SCI

摘要： Aiming at the existing problems in the production and export scale prediction of aquaculture, a model of yield prediction based on BP Neural network algorithm is proposed, and a set of algorithms is proposed to optimize BP neural network (BPNN). Based on the traditional BP neural network, it is easy to get into the local optimal problem due to the long training time of the model. By using the simple Johnson algorithm, the dimensionality of the input neuron is reduced, and then the hidden layer neural network is determined by this method. At the same time, the data mining method is used to filter the Data.Particle swarm optimization algorithm is used to optimize the parameters. At the same time, based on the domestic e-commerce Sales network data, the results show that the average square root error of the model is less than the traditional BP neural network and the learning efficiency is higher than the traditional BP neural network. The results show that the model has a great advantage in building up a large number of historical data, and it can shorten the modeling time and get good prediction result by combining the sales data of e-commerce. It provides a new feasible method for the export prediction of aquatic products. (C) 2018 Elsevier B.V. All rights reserved.