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

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 materi...

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Main Authors:	Rahman, Muhammad Shafiur, Rashid, Muhammad Mahbubur, Hussain, Mohamed Azlan
Format:	Article
Language:	English
Published:	0960-3085 2012
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://irep.iium.edu.my/15026/ http://irep.iium.edu.my/15026/ http://irep.iium.edu.my/15026/ http://irep.iium.edu.my/15026/1/thermal.pdf

id	iium-15026
recordtype	eprints
spelling	iium-150262014-07-17T02:30:18Z http://irep.iium.edu.my/15026/ Thermal conductivity prediction of foods by Neural Network and Fuzzy (ANFIS) modeling techniques Rahman, Muhammad Shafiur Rashid, Muhammad Mahbubur Hussain, Mohamed Azlan TJ Mechanical engineering and machinery 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. 0960-3085 2012 Article PeerReviewed application/pdf en http://irep.iium.edu.my/15026/1/thermal.pdf 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 http://www.sciencedirect.com/science/article/pii/S0960308511000599# 10.1016/j.fbp.2011.07.001
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Rahman, Muhammad Shafiur Rashid, Muhammad Mahbubur Hussain, Mohamed Azlan Thermal conductivity prediction of foods by Neural Network and Fuzzy (ANFIS) modeling techniques
description	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.
format	Article
author	Rahman, Muhammad Shafiur Rashid, Muhammad Mahbubur Hussain, Mohamed Azlan
author_facet	Rahman, Muhammad Shafiur Rashid, Muhammad Mahbubur Hussain, Mohamed Azlan
author_sort	Rahman, Muhammad Shafiur
title	Thermal conductivity prediction of foods by Neural Network and Fuzzy (ANFIS) modeling techniques
title_short	Thermal conductivity prediction of foods by Neural Network and Fuzzy (ANFIS) modeling techniques
title_full	Thermal conductivity prediction of foods by Neural Network and Fuzzy (ANFIS) modeling techniques
title_fullStr	Thermal conductivity prediction of foods by Neural Network and Fuzzy (ANFIS) modeling techniques
title_full_unstemmed	Thermal conductivity prediction of foods by Neural Network and Fuzzy (ANFIS) modeling techniques
title_sort	thermal conductivity prediction of foods by neural network and fuzzy (anfis) modeling techniques
publisher	0960-3085
publishDate	2012
url	http://irep.iium.edu.my/15026/ http://irep.iium.edu.my/15026/ http://irep.iium.edu.my/15026/ http://irep.iium.edu.my/15026/1/thermal.pdf
first_indexed	2023-09-18T20:24:04Z
last_indexed	2023-09-18T20:24:04Z
_version_	1777408298479255552

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

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