Investigation of feedforward backpropagation artificial neural networks

Artificial neural networks are networks that are inspired by the biological nervous system and connected to each other in different ways. These networks have the ability for storage, learning and using of information. Thus, artificial neural networks can be used for classification, estimation, and association as well as providing in the most appropriate form. In this study, the algorithms related to feedforward back propagation artificial neural networks were introduced and performed an application with a data set. By 81 explanatory variables, 1111 data of Super-Conductor dataset that achieved from free-access Machine Learning Repository website (UCI) were used in the application. By considering number of neurons in the interlayer as 25, 50 and 75, a total of 13 applications were performed with MATLAB package program for the algorithms. 1000 iterations were performed for each application by taking 70% as training and 30% as validation and test of the data set. In the applications, correlations between the original and output values were evaluated. By excepting traingd, traingda, traingdm and trainrp algoritms, the highest correlation was found as 95.46% in trainbr algorithm while the lowest one 70.28% in traingdx algorithm for the training, validation and test data sets. Key words: Algorithm, interlayer, iteration, activation function, training data