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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| Format: | Article |
| Language: | English |
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0960-3085
2012
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| Online Access: | http://irep.iium.edu.my/15026/ http://irep.iium.edu.my/15026/1/thermal.pdf |
| _version_ | 1848777757761208320 |
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| 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 |
| building | IIUM Repository |
| collection | Online Access |
| 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.
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| first_indexed | 2025-11-14T14:51:04Z |
| format | Article |
| id | iium-15026 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T14:51:04Z |
| publishDate | 2012 |
| publisher | 0960-3085 |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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 |
| 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 |
| title | 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_short | 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 |
| topic | TJ Mechanical engineering and machinery |
| 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 |