Prediction of Thermal Performance of Unidirectional Flow Porous Bed Solar Air Heater with Optimal Training Function Using Artificial Neural Network

In the present work, Artificial Neural Network (ANN) has been used to predict the thermal performance of unidirectional flow porous bed solar air heater. The ANN model was structured on the basis of data sets obtained from experiments and values of thermal efficiency of solar air heater. Four types of training functions are used in ANN model for training process with feed forward learning procedure. The aim of this work is to examine the performance and comparison of four training functions (TRAINCGP, TRAINSCG, TRAINLM and TRAINOSS) applied in training process of neural model. A comparison was based on the RMSE and R2. It was found that training function TRAINLM exhibits optimal result with the experimental data.