An Algorithm For Training Multilayer Perceptron (MLP) For Image Reconstruction Using Neural Network Without Overfitting

Abstract: Recently, back propagation neural network (BPNN) has been applied successfully in many areas with excellent generalization results, for example, rule extraction, classification and evaluation. In this paper the Levenberg-Marquardt back-propagation algorithm is used for training the network and reconstructs the image. It is found that Marquardt algorithm is significantly more proficient. A practical problem with MLPs is to select the correct complexity for the model, i.e., the right number of hidden units or correct regularization parameters. In this paper, a study is made to determine the issue of number of neurons in every hidden layer and the quantity of hidden layers needed for getting the high accuracy. We performed regression R analysis to measure the correlation between outputs and targets