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2011 (Vol. 3, Issue: 05)
Article Information:

An Intelligent Demand Forecasting Model with Back Propagation Neural Network for Fish Product

Chih-Yao Lo, Cheng-I Hou and Yi-Yun Pai
Corresponding Author:  Chih-Yao Lo 

Key words:  Back propagation neural network algorithm, fishery demand, forecasting system, , , ,
Vol. 3 , (05): 447-455
Submitted Accepted Published
2011 April, 04 2011 May, 18 2011 May, 25
Abstract:

Taiwan joined the WTO since 2000, five years later the agricultural and fishing products can be freely imported. Faced with such a shock, how to reduce the production cost of agricultural or fishery products would be an important issue. In the low-profit era, production costs often affect the operation of enterprises in an important factor. In particular, the production cost of agricultural and fishing products, particularly significant. The operating personnel need spend much capital on the stock of goods in the traditional fish industry. However, the price of fish product is changing daily based on the supply and demand in the market. The operating personnel can buy at low price and achieve the objective of short-term stock according to the short-term demand if the information technology can be used to assist them to forecast the demand in the future. It can not only increase the profit, but also enable the backward customer get the fresh fish product with low price and assist them to reduce the material cost. Therefore, this study has the back-propagation neural network predict. Enterprises using case history of each fishery products, orders goods sales records forecast future purchases. Establishing the future order prediction model in fisheries products, in order to achieve the purpose of reducing production costs and enhance their competitiveness.
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  Cite this Reference:
Chih-Yao Lo, Cheng-I Hou and Yi-Yun Pai, 2011. An Intelligent Demand Forecasting Model with Back Propagation Neural Network for Fish Product.  Research Journal of Applied Sciences, Engineering and Technology, 3(05): 447-455.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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