Response surface optimization of an artificial neural network for predicting the size of re-assembled casein micelles

Ashkan Madadlou, Zahra Emam-Djomeh, Mohamad Ebrahimzadeh Mousavi, Mohamadreza Ehsani, Majid Javanmard, David Sheehan

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

An artificial neural network (ANN) was designed to predict the size of re-assembled micelles in casein solutions as influenced by pH of solution and ultrasonic treatment. A generalized feed-forward network consisted of five neurons in the input layer, one hidden layer and an output layer with one neuron optimized using response surface methodology (RSM). Number of hidden neurons, momentum coefficient and step size in the hidden layer, number of epochs and training runs were the variables optimized. A quadratic equation was applied to mean absolute error (MAE) of 52 artificial neural networks as the response. It was found that the first-order effect of epoch number is the most significant term in determination of MAE, followed by the interactive effect of epoch number and step size. Minimum response (MAE) was obtained by employing the following optimum conditions for the artificial neural network: hidden neurons number = 10, momentum coefficient = 0.6, step size = 0.34, epoch number = 6230 and training run = 1.

Original languageBritish English
Pages (from-to)216-221
Number of pages6
JournalComputers and Electronics in Agriculture
Volume68
Issue number2
DOIs
StatePublished - Oct 2009

Keywords

  • Artificial neural network
  • Re-assembled casein micelles
  • Response surface method

Fingerprint

Dive into the research topics of 'Response surface optimization of an artificial neural network for predicting the size of re-assembled casein micelles'. Together they form a unique fingerprint.

Cite this