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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
0960-3085
2012
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| Subjects: | |
| 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 |
| Summary: | 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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