Thermal conductivity prediction of foods by Neural Network and Fuzzy (ANFIS) modeling techniques

Rahman, Muhammad Shafiur and Rashid, Muhammad Mahbubur and Hussain, Mohamed Azlan (2012) Thermal conductivity prediction of foods by Neural Network and Fuzzy (ANFIS) modeling techniques. Food and Bioproducts Processing, 90 (2). pp. 333-340. ISSN 09603085

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Abstract

A neuro-fuzzy modeling technique was used to predict the effective of thermal conductivity of various fruits and vegetables. A total of 676 data point was used to develop the neuro-fuzzy model considering the inputs as the fraction of water content, temperature and apparent porosity of food materials. The complexity of the data set which incorporates wide ranges of temperature (including those below freezing points) made it difficult for the data to be predicted by normal analytical and conventional models. However the adaptive neuro-fuzzy model (ANFIS) was able to predict conductivity values which closely matched the experimental values by providing lowest mean square error compared to multivariable regression and conventional artificial neural network (ANN) models. This method also alleviates the problem of determining the hidden structure of the neural network layer by trial and error. © 2011 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Item Type:	Article (Journal)
Additional Information:	5486/15026
Uncontrolled Keywords:	Fuzzy model; Artificial neural network; Neurofuzzy; Porosity; Thermal conductivity
Subjects:	T Technology > TJ Mechanical engineering and machinery
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User:	Dr Muhammad Rashid
Date Deposited:	14 Mar 2014 16:25
Last Modified:	17 Jul 2014 10:30
URI:	http://irep.iium.edu.my/id/eprint/15026

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