Neural network optimization by comparing the performances of the training functions -Prediction of heat transfer from horizontal tube immersed in gas-solid fluidized bed

This paper describes the selection of training function of an artificial neural network (ANN) for modeling the heat transfer prediction of horizontal tube immersed in gas-solid fluidized bed of large particles. The ANN modeling was developed to study the effect of fluidizing gas velocity on the average heat transfer coefficient between fluidizing bed and horizontal tube surface. The feed-forward network with back propagation structure implemented using Levenberg-Marquardt's learning rule in the neural network approach. The objective of this work is to compare performances of five training functions (TRAINSCG, TRAINBFG, TRAINOSS, TRAINLM and TRAINBR) implemented in training neural network for predicting the heat transfer coefficient. The comparison is shown on the basis of percentage relative error, coefficient of determination, root mean square error and sum of the square error. The predictions by training function TRAINBR found to be in good agreement with the experiment's values.