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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\u2019s 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.<\/p>","DOI":"10.4018\/ijfsa.2013010101","type":"journal-article","created":{"date-parts":[[2013,4,9]],"date-time":"2013-04-09T13:44:28Z","timestamp":1365515068000},"page":"1-24","source":"Crossref","is-referenced-by-count":7,"title":["Comparative Analysis of Artificial Neural Networks and Neuro-Fuzzy Models for Multicriteria Demand Forecasting"],"prefix":"10.4018","volume":"3","author":[{"given":"Golam","family":"Kabir","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, University of British Columbia, Kelowna, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. 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