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Prediction of Vegetable Price Based on Neural Network and Genetic Algorithm

文献类型： 会议论文

第一作者： Changshou Luo

作者： Changshou Luo 1 ; Qingfeng Wei 1 ; Liying Zhou 2 ; Junfeng Zhang 1 ; Sufen Sun 1 ;

作者机构： 1.Institute of Information on Science and Technology of Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, P.R. China

2.China Agricultural University Library, Beijing 100094, P.R. China

关键词： genetic algorithm;neural network;prediction;vegetables price

会议名称： IFIP TC 12 conference on computer and computing technologies in agriculture

主办单位：

页码： 672-681

摘要： In this paper, the theory and construction methods of four models are presented for predicting the vegetable market price, which are BP neural network model, the neural network model based on genetic algorithm, RBF neural network model and an integrated prediction model based on the three models above. The four models are used to predict the Lentinus edodes price for Beijing Xinfadi wholesale market. A total of 84 records collected between 2003 and 2009 were fed into the four models for training and testing. In summary, the predicting ability of BP neural network model is the worst. The neural network model based on genetic algorithm was generally more accurate than RBF neural network model. The integrated prediction model has the best results.

分类号： S126

  • 相关文献

[1]Prediction of Vegetable Price Based on Neural Network and Genetic Algorithm. Zhou, Liying,Luo, Changshou,Wei, Qingfeng,Zhang, Junfeng,Sun, Sufen. 2011

[2]Monitoring The Chlorophyll Fluorescence Parameters In Rice Under Flooding And Waterlogging Stress Based On Remote Sensing. Gu, Xiaohe,Xu, Peng,Feng, Haikuan,Gu, Xiaohe,Xu, Peng,Feng, Haikuan,Xu, Peng,Qiu, He. 2014

[3]Simulating soil water and solute transport in a soil-wheat system using an improved genetic algorithm. Luo, Changshou,Sun, Sufen,Zhang, Junfeng,Zuo, Qiang,Li, Baoguo. 2008

[4]Retrieval of LAI and leaf chlorophyll content from remote sensing data by agronomy mechanism knowledge to solve the ill-posed inverse problem. Li, Zhenhai,Nie, Chenwei,Yang, Guijun,Xu, Xingang,Jin, Xiuliang,Gu, Xiaohe. 2014

[5]Retrieval of LAI and leaf chlorophyll content from remote sensing data by agronomy mechanism knowledge to solve the ill-posed inverse problem. Zhenhai Li,Chenwei Nie,Guijun Yang,Xingang Xu,Xiuliang Jin,Xiaohe Gu. 2014

[6]Self-adaptive Variable Structure Control for Autonomous Guided Agricultural Vehicle. Zhou Jianjun,WangXiu,Chen Liping,Deng Wei,Cai Jichen. 2014

[7]Simulating Soil Water and Solute Transport in a Soil-wheat System Using an Improved Genetic Algorithm. Changshou Luo,Sufen Sun,Junfeng Zhang,Qiang Zuo,Baoguo Li. 2008

[8]SIMULATING SOIL WATER AND SOLUTE TRANSPORT IN A SOIL-WHEAT SYSTEM USING AN IMPROVED GENETIC ALGORITHM. Changshou Luo,Sufen Sun,Junfeng Zhang,Qiang Zuo,Baoguo Li. 2008

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