The Impact of Training Iterations on ANN Applications Using BPNN Algorithm
Training Artificial Neural Network (ANN) has attracted many researchers for a long time. This paper investigates the impact of training iterations of ANN using Back-Propagation Neural Network (BPNN) algorithm. The two sets of adjustable parameters, i.e., the learning rate and number of hidden nodes in the hidden layer are used to analyze the impact of training iterations of ANN applications is used. The applications that are used in this research are XOR problem and digit recognition. The efficacy of the results using BPNN algorithm is shown through an analysis of the impact of training iterations and by presenting simulation results from two different applications.