The training of RBFNN involves two steps: 1) the centres need to be selected for training and 2) weight between hidden layer and output layer is calculated. In contrast to the multiple layer structure of BPNN, RBFNN has a simpler three-layer structure. By using radial function that only focuses on the distance between inputs and fixed points, RBFNN prioritizes distance over exact location, making it less sensitive to noise and variation. These enable RBFNN to deliver more reliable approximation result with less efforts in model establishment and tuning .