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

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 training, 0.0399 and 0.95243 for testing and 0.0264 and 0.95151 for validation data sets. The results of the testing model on new samples showed excellent agreement between the actual and predicted data with coefficient of determination R 2 = 0.97.

Modelling of dried apple rehydration indices using ANN

“…Rehydration lasted from 6 h (at a medium temperature amounting to 20°C) to 2 h (for 95°C), and was carried out in triplicate. The initial mass of each dried sample subjected to rehydration was 10 g, and the dried sample mass to water mass ratio at the beginning of the rehydration process was 1:20 (such a ratio was used very frequently in the literature (Femenia et al, 2000;Ravindra and Chattopadhyay, 2000;Winiczenko et al, 2018)). The water temperature was constant, and it was not stirred during rehydration.…”

“…Therefore, such networks are very useful for modelling certain processes that are not yet completely understood (Mittal, 1996). Winiczenko et al (2018) used a genetic algorithm and response surface methodology to optimize an ANN topology for predicting the colour change in rehydrated apple.…”

Apple Cubes Drying and Rehydration. Multiobjective Optimization of the Processes

“…Chosen cases (114) were randomly divided into training-77 samples (70%), validation-17 samples (15%), and testing-17 samples (15%) sets. ANNs were implemented in MATLAB and the Levenberg-Marquardt algorithm was used for training [50].…”

Extension of Experimentally Assembled Processing Maps of 10CrMo9-10 Steel via a Predicted Dataset and the Influence on Overall Informative Possibilities

“…The ANN approach is part of a wide family of biology-inspired mathematical techniques that are intended to solve complicated scientific and engineering tasks, e.g., highly nonlinear approximation issues [25]. These mathematical approaches have been found to be very useful in various applications (see e.g., [26][27][28]).…”