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

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, DOI

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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.

Item Type: Article
Additional Information: 931BD Times Cited:0 Cited References Count:40
Uncontrolled Keywords: Artificial neural network, Fuzzy model, Neurofuzzy, Porosity, Thermal conductivity, Apparent porosity, Conventional artificial neural network models, Conventional models, Data points, Data sets, Experimental values, Food materials, Freezing point, Fuzzy models,, Hidden structures, Modeling technique, Multivariable regression, Neuro-Fuzzy, Neuro-Fuzzy model, Neuro-fuzzy modeling, Trial and error, Forecasting, Network layers, Neural networks.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TP Chemical technology
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 10 Jul 2013 00:49
Last Modified: 27 Nov 2019 06:23

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