|Table of Contents|

Soybean Price Prediction in China Based on Improved RBF Neural Network(PDF)

《大豆科学》[ISSN:1000-9841/CN:23-1227/S]

Issue:
2016年02期
Page:
310-314
Research Field:
Publishing date:

Info

Title:
Soybean Price Prediction in China Based on Improved RBF Neural Network
Author(s):
SHI Bo;  ZHANG Dong-qing;  MA Kai-ping;  LIU Huan
College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Keywords:
Soybean;  Price;  RBF Neural Network;  Genetic algorithm
PACS:
-
DOI:
10.11861/j.issn.1000-9841.2016.02.0310
Abstract:
Soybean price in China is influenced by various factors at home and abroad.It is difficult to analyze and predict soybean price using the traditional mathematical model for which has the characteristics of nonlinearity, randomness and high noise. RBF Neural Network is widely used in nonlinear time series prediction for its excellent approximation performance.In this paper, one price prediction model of soybean is proposed based on improved RBF Neural Network model, which is multivariable predictive model with multidimensional inputs and one-dimensional output.The initial inputs of model consist of historical data and the influencing factors of soybean.The node numbers of input layer, the centers and widths of Gaussian kernel and output layer weights are optimized by genetic algorithm.The improved model can select the most appropriate variables as inputs of the model automatically from the initial inputs.By using the data of soybean price between 2009 and 2014 to train and forecast. The improved RBF Neural Network select soybean imports of China, consumer confidence index of China and the distribution price in port of imported soybeans as the inputs of the related influence factors through automatically identification.The model’s prediction error is 3.64%, prediction results showed that the model prediction accuracy was high and generalization ability was strong.The model can capture the change rule of soybean price accurately.The model could provide reference for the accurate prediction of soybean market price.

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Last Update: 2016-04-04