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Comparative Analysis of Artificial Neural Networks and Neuro-Fuzzy Models for Multicriteria Demand Forecasting

Author

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  • Golam Kabir

    (Department of Civil Engineering, University of British Columbia, Kelowna, BC, Canada)

  • M. Ahsan Akhtar Hasin

    (Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh)

Abstract

An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. The objective of the paper is to propose a new forecasting mechanism which is modeled by artificial intelligence approaches including the comparison of both artificial neural networks (ANN) and adaptive network-based fuzzy inference system (FIS) techniques to manage the fuzzy demand with incomplete information. Artificial neural networks has been applied as it is capable to model complex, nonlinear processes without having to assume the form of the relationship between input and output variables. Neuro-fuzzy systems also utilized to harness the power of the fuzzy logic and ANNs through utilizing the mathematical properties of ANNs in tuning rule-based fuzzy systems that approximate the way human’s process information. The effectiveness of the proposed approach to the demand forecasting issue is demonstrated for a 20/25 MVA Distribution Transformer from Energypac Engineering Limited (EEL), a leading power engineering company of Bangladesh.

Suggested Citation

  • Golam Kabir & M. Ahsan Akhtar Hasin, 2013. "Comparative Analysis of Artificial Neural Networks and Neuro-Fuzzy Models for Multicriteria Demand Forecasting," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 3(1), pages 1-24, January.
    • Handle: RePEc:igg:jfsa00:v:3:y:2013:i:1:p:1-24
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