Intelligent Artificial Neural Network computing models for predicting shelf life of processed cheese

Elman artificial neural network models with single and multilayer for predicting shelf life of processed cheese stored at 7-8ºC were developed. Input parameters were: Body & texture, aroma & flavour, moisture, and free fatty acid, while sensory score was output parameter. Bayesian regularization was training algorithm for the models. The network was trained up to 100 epochs, and neurons in each hidden layers varied from 1 to 20. Transfer function for hidden layer was tangent sigmoid, while for the output layer it was pure linear function. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were used for comparing the prediction ability of the developed models. Elman model with combination of 4-17-17-1 performed significantly well for predicting the shelf life of processed cheese stored at 7-8º C.

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Artificial Neural Expert Computing Models for Determining Shelf Life of Processed Cheese

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v2i3.353 ◽

2012 ◽

Vol 2 (3) ◽

Cited By ~ 1

Author(s):

SUMIT GOYAL ◽

Gyanendra Kumar Goyal

Keyword(s):

Shelf Life ◽

Processed Cheese ◽

Artificial Neural ◽

Computing Models

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The Prediction of Needle Penetration Force in Woven Denim Fabrics Using Soft Computing Models

Journal of Engineered Fibers and Fabrics ◽

10.1177/155892501400900406 ◽

2014 ◽

Vol 9 (4) ◽

pp. 155892501400900 ◽

Cited By ~ 3

Author(s):

Ezzatollah Haghighat ◽

Saeed Shaikhzadeh Najar ◽

Seyed Mohammad Etrati

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fuzzy Logic ◽

Soft Computing ◽

Logic Model ◽

Needle Penetration ◽

Fuzzy Logic Model ◽

Penetration Force ◽

Artificial Neural ◽

Computing Models

The aim of this paper was to predict the needle penetration force in denim fabrics based on sewing parameters by using the fuzzy logic (FL) model. Moreover, the performance of fuzzy logic model is compared with that of the artificial neural network (ANN) model. The needle penetration force was measured on the Instron tensile tester. In order to plan the fuzzy logic model, the sewing needle size, number of fabric layers and fabric weight were taken into account as input parameters. The output parameter is needle penetration force. In addition, the same parameters and data are used in artificial neural network model. The results indicate that the needle penetration force can be predicted in terms of sewing parameters by using the fuzzy logic model. The difference between performance of fuzzy logic and neural network models is not meaningful ( RFL=0.971 and RANN=0.982). It is concluded that soft computing models such as fuzzy logic and artificial neural network can be utilized to forecast the needle penetration force in denim fabrics. Using the fuzzy logic model for predicting the needle penetration force in denim fabrics can help the garment manufacturer to acquire better knowledge about the sewing process. As a result, the sewing process may be improved, and also the quality of denim apparel increased.

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