Optimization of an artificial neural network topology using coupled response surface methodology and genetic algorithm for fluidized bed drying

In this study, an integrated response surface methodology (RSM) and genetic algorithm (GA) are recommended for developing artificial neural networks (ANNs) with great chances to be an optimal one. A multi-layer feed forward (MLFF) ANN was applied to correlate the outputs (energy and exergy) to the four exogenous inputs (drying time, drying air temperature, carrot cubes size, and bed depth). The RSM was used to build the relationship between the input parameters and output responses, and used as the fitness function to measure the fitness value of the GA approach. In the relationship building, five variables were used (number of neurons, momentum coefficient and step size in the hidden layer, number of epochs and number of training times). A polynomial model was developed from training results to mean square error (MSE) of 50 developed ANNs to generate 3D response surfaces and contour plots. Finally, GA was applied to find the optimal topology of ANN. The ANN topology had minimum MSE when the number of neurons in the hidden layer, momentum coefficient, step size, number of training epochs and training times were 28, 0.66, 0.35, 2877 and 3, respectively. The energy and exergy of carrot cubes during fluidized bed drying were predicted with R2 values of greater than 0.97 using optimal ANN topology.

Research highlights▶ Coupled response surface methodology (RSM) and genetic algorithm (GA) successfully applied to find the global optimum topology of artificial neural network (ANN). ▶ This approach (RSM with GA) improved the ANN performance and accelerated the model development. ▶ The global optimum topology of ANN precisely predicted energy and exergy of carrot cubes during fluidized bed drying.