Home           Contact us           FAQs           
     Journal Home     |     Aim & Scope    |    Author(s) Information      |     Editorial Board     |     MSP Download Statistics
2013 (Vol. 5, Issue: 07)
Article Information:

Construction of a Health Food Demand Prediction Model Using a Back Propagation Neural Network

Han-Chen Huang
Corresponding Author:  Han-Chen Huang 

Key words:  Artificial neural network, demand prediction, particle swarm optimization algorithm, , , ,
Vol. 5 , (07): 896-899
Submitted Accepted Published
March 19, 2013 April 02, 2013 July 05, 2013

For business operations, determining market demands is necessary for enterprises in establishing appropriate purchase, production and sales plans. However, many enterprises lack this ability, causing them to make risky purchasing decisions. This study combines a back propagation neural network and the Particle Swarm Optimization Algorithm (PSOBPN) to construct a demand prediction model. Using a grey relational analysis, we selected factors that have a high correlation to market demands. These factors were employed to train the prediction model and were used as input factors to predict market demands. The results obtained from the prediction model were compared with those of the experiential estimation model used by health food companies. The comparison showed that the accuracy of PSOBPN predictions was superior to that of the experiential estimation method. Therefore, the prediction model proposed in this study provides reliable and highly efficient analysis data for decision-makers in enterprises.
Abstract PDF HTML
  Cite this Reference:
Han-Chen Huang, 2013. Construction of a Health Food Demand Prediction Model Using a Back Propagation Neural Network.  Advance Journal of Food Science and Technology, 5(07): 896-899.
    Advertise with us
ISSN (Online):  2042-4876
ISSN (Print):   2042-4868
Submit Manuscript
   Current Information
   Sales & Services
Home  |  Contact us  |  About us  |  Privacy Policy
Copyright © 2015. MAXWELL Scientific Publication Corp., All rights reserved