Time – Delay Simulated Artificial Neural Network Models for Predicting Shelf Life of Processed Cheese | Zendy

Sumit Goyal | Zendy; Gyanendra Kumar Goyal | Zendy

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Time – Delay Simulated Artificial Neural Network Models for Predicting Shelf Life of Processed Cheese

Author(s) -

Sumit Goyal,

Gyanendra Kumar Goyal

Publication year - 2012

Publication title -

international journal of intelligent systems and applications

Language(s) - English

Resource type - Journals

eISSN - 2074-9058

pISSN - 2074-904X

DOI - 10.5815/ijisa.2012.05.05

Subject(s) - computer science , mean squared error , artificial neural network , shelf life , coefficient of determination , bayesian probability , algorithm , artificial intelligence , machine learning , statistics , mathematics , food science , chemistry

This paper highlights the significance of Time-Delay ANN models for predicting shelf life of processed cheese stored at 7-8 o C. Bayesian regularization algorithm was selected as training function. Number of neurons in single and multiple hidden layers varied from 1 to 20. The network was trained with up to 100 epochs. Mean square error, root mean square error, coefficient of determination and nash Sutcliffe coefficient were used for calculating the prediction capability of the developed models. TimeDelay ANN models with multilayer are quite efficient in predicting the shelf life of processed cheese stored at 78 o C.

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