Optimisation of ANN topology for predicting the rehydrated apple cubes colour change using RSM and GA

Neural Computing & Applications
Radosław WiniczenkoMonika Janaszek-Mańkowska

Abstract

In this study, an efficient optimisation method by combining response surface methodology (RSM) and genetic algorithm (GA) is introduced to find the optimal topology of artificial neural networks (ANNs) for predicting colour changes in rehydrated apple cubes. A multi-layered feed-forward backpropagation ANN model of algorithms was developed to correlate one output (colour change) to four input variables (drying air temperature, drying air velocity, temperature of distilled water and rehydration time). A predictive model for ANN topology in terms of the best mean squared error (MSE) performance on validation samples was created using RSM. RSM model was integrated with an effective GA to find the optimum topology of ANN. The optimum ANN had minimum MSE when the number of hidden neurons, learning rate, momentum constant, number of epochs and number of training runs were 13, 0.33, 0.89, 3869 and 3, respectively. MSE of optimal ANN topology on validation samples was 0.0072095. It turned out that the optimal ANN topology can be considered as more precise for predicting colour change in the rehydrated apple cubes. Mean absolute error and regression coefficient (R) of the optimal ANN topology were determined as 0.0259 and 0.96475 for t...Continue Reading

References

Nov 23, 2006·Archives of Clinical Neuropsychology : the Official Journal of the National Academy of Neuropsychologists·Tresa Roebuck-SpencerJoseph Bleiberg
Jan 1, 1996·IEEE Transactions on Neural Networks·K Liano

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