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 material...
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my.um.eprints.69862019-11-27T06:23:14Z http://eprints.um.edu.my/6986/ Thermal conductivity prediction of foods by Neural Network and Fuzzy (ANFIS) modeling techniques Rahman, Mohammad Shafiur Rashid, M.M. Hussain, Mohd Azlan TA Engineering (General). Civil engineering (General) TP Chemical technology 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. Elsevier 2012 Article PeerReviewed Rahman, Mohammad Shafiur and Rashid, M.M. and Hussain, Mohd 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 0960-3085 https://doi.org/10.1016/j.fbp.2011.07.001 doi:10.1016/j.fbp.2011.07.001 |
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TA Engineering (General). Civil engineering (General) TP Chemical technology Rahman, Mohammad Shafiur Rashid, M.M. Hussain, Mohd Azlan Thermal conductivity prediction of foods by Neural Network and Fuzzy (ANFIS) modeling techniques |
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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. |
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Article |
author |
Rahman, Mohammad Shafiur Rashid, M.M. Hussain, Mohd Azlan |
author_facet |
Rahman, Mohammad Shafiur Rashid, M.M. Hussain, Mohd Azlan |
author_sort |
Rahman, Mohammad 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 |
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Elsevier |
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2012 |
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http://eprints.um.edu.my/6986/ https://doi.org/10.1016/j.fbp.2011.07.001 |
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