预测模型在木薯产品供需管理中的应用

King Mongkut’s University of Technology North Bangkok International Journal of Applied Science and Technology Pub Date : 2016-05-31 DOI:10.14416/J.IJAST.2016.05.003

Natthaya Choosuk, A. Kengpol

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引用次数: 2

摘要

本研究的目的是生成能够有效预测四种木薯产品供需的模型。适宜的木薯产量预测模型为Back Propagation Neural Network (BPN) 4-14-1、木薯淀粉预测模型为BPN 7-12-1、木薯切片预测模型为BPN 7-14-1、木薯颗粒预测模型为多元线性回归(MLP)模型、西米预测模型为BPN 7-13-1。然后，利用线性规划方法计算木薯产品的优化，以获得最大的利润，木薯种植面积获得最大的单产。这项研究的好处可以支持农民和制造商的管理规划。

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An Application of Forecasting Models for the Supply and Demand Management of Cassava Products

The objectives of this research are to generate models that can effectively forecast the supply and demand of four cassava products. The appropriate forecasting models for cassava production volume is Back Propagation Neural Network (BPN) 4-14-1, cassava starch is BPN 7-12-1, cassava chip is BPN 7-14-1, cassava pellets is Multiple Linear Regression (MLP), and sago is BPN 7-13-1. Then, Linear Programming is used to calculate the optimization of cassava products to obtain the maximum profit and for cassava plant areas to obtain the maximum yield per area. The benefits of this research can support management planning for farmers and manufacturers.

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King Mongkut’s University of Technology North Bangkok International Journal of Applied Science and Technology

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