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Journal of Applied Sciences
  Year: 2008 | Volume: 8 | Issue: 2 | Page No.: 346-351
DOI: 10.3923/jas.2008.346.351
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Estimation of Paddy Equilibrium Moisture Sorption Using ANNs

R. Amiri Chayjan and Y. Moazez

In this research, Artificial Neural Networks (ANNs) used for prediction of Equilibrium Moisture Content (EMC) of three varieties of paddy (Sadri, Tarom and Khazar) as a new method. Feed forward back propagation and cascade forward back propagation networks with Levenberg-Marquardt and Bayesian regularization training algorithms used for training of input patterns. Optimized trained network has the ability of EMC prediction to test patterns at thermal boundary of 20-40 °C and relative humidity boundary of 13.5-87% with R2 = 0.9929 and mean absolute error 0.0229. Comparison between optimized ANN result and empirical model of Henderson showed that artificial neural network not only can simultaneously predict the EMC of samples of all varieties but also has better coefficient of determination and less mean absolute error.
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  •    Determination the Factors Explaining Variability of Physical Soil Organic Carbon Fractions using Artificial Neural Network
How to cite this article:

R. Amiri Chayjan and Y. Moazez, 2008. Estimation of Paddy Equilibrium Moisture Sorption Using ANNs. Journal of Applied Sciences, 8: 346-351.

DOI: 10.3923/jas.2008.346.351


04 June, 2010
uma maheswari:

Dear Amiri,
I have cited your research ,its a new technology ,but still you can give more details on contamination of bacillus cereus toxins in parboiled rice.




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