Using an Artificial Neural Network to Forecast the Market Share of Thai Rice

被引：1

作者：

Apichottanakul, A. ^{[1
]}

Piewthongngam, K. ^{[2
]}

Pathumnakul, S. ^{[3
]}

机构：

[1] Khon Kaen Univ, Grad Sch, Khon Kaen, Thailand

[2] Khon Kaen Univ, Fac Management Sci, Esaan Ctr Business & Econ Res, Khon Kaen, Thailand

[3] Khon Kaen Univ, Fac Engn, Dept Ind Engn, Khon Kaen, Thailand

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, VOLS 1-4 | 2009年

关键词：

Neural networks; back propagation; demand for rice; market share; POWDER-METALLURGY MATERIALS; PARAMETERS; SELECTION; MODEL;

D O I：

10.1109/IEEM.2009.5373247

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this paper, the artificial neural networks (ANN) is used to estimate the market share of Thai rice in the global market. Two models are formulated under two assumptions. First, the market share depending on exporting prices of rice of Thailand, Vietnam, India, USA, Pakistan, China. Second, only the, export prices of rice from Thailand, Vietnam, USA, and China are considered. The export prices are used as input parameters, while the market share of Thai's rice in the global market is the only output parameter of the models. Annual data from 1980 to 2005 are gathered from United States Department of Agriculture (USDA) and Food and Agriculture Organization of the United Nations (FAO). The study showed that the second model provide more promising results with the minimum mean absolute percent error (MAPE) of 4.69% and the average MAPE of 10.92%.

引用

页码：665 / 668

页数：4

共 15 条

[1] Market share forecasting: An empirical comparison of artificial neural networks and multinomial logit model
Agrawal, D
Schorling, C
[J]. JOURNAL OF RETAILING, 1996, 72 (04) : 383 - 407
[2] A neural network approach for selection of powder metallurgy materials and process parameters
Cherian, RP
Smith, LN
Midha, PS
[J]. ARTIFICIAL INTELLIGENCE IN ENGINEERING, 2000, 14 (01): : 39 - 44
[3] CO HC, 2007, COMPUT IND ENG, V53, P1140
[4] Application of neural computing in basic oxygen steelmaking
Cox, IJ
Lewis, RW
Ransing, RS
Laszczewski, H
Berni, G
[J]. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2002, 120 (1-3) : 310 - 315
[5] *FAO, 2004, 12 RIC AN MOD RES
[6] Neural modeling helps the BOS process to achieve aimed end-point conditions in liquid steel
Fileti, AMF
Pacianotto, TA
Cunha, AP
[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2006, 19 (01) : 9 - 17
[7] Using an artificial neural network trained with a genetic algorithm to model brand share
Fish, KE
Johnson, JD
Dorsey, RE
Blodgett, JG
[J]. JOURNAL OF BUSINESS RESEARCH, 2004, 57 (01) : 79 - 85
[8] Neural network based manufacturability evaluation of free form machining
Korosec, M
Balic, J
Kopac, J
[J]. INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 2005, 45 (01) : 13 - 20
[9] Neural network prediction of a storm surge
Lee, TL
[J]. OCEAN ENGINEERING, 2006, 33 (3-4) : 483 - 494
[10] Using intelligent software to predict the effects of formulation and processing parameters on roller compaction
Mansa, Rachel F.
Bridson, Rachel H.
Greenwood, Richard W.
Barker, Helen
Seville, Jonathan P. K.
[J]. POWDER TECHNOLOGY, 2008, 181 (02) : 217 - 225

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