Behaviour Analysis of Multilayer Perceptronswith Multiple Hidden Neurons and Hidden Layers

Behavioral analysis of different number of hidden layers and differentNumber of hidden neurons is discussed, it's very difficult to select number ofhidden layers and hidden neurons.

Abstract

The terms (NN) and Artificial Neural (ANN) usually refer to a Multilayer Perceptron Network. It process the records one at a time, and learn by comparing their prediction of the record with the known actual record. The problem of model selection is considerably important for acquiring higher levels of generalization capability in supervised learning. This paper discussed behavioral analysis of different number of hidden layers and different number of hidden neurons. It's very difficult to select number of hidden layers and hidden neurons. There are different methods like Akaike's Information Criterion, Inverse test method and some traditional methods are used to find Neural Network architecture. What to do while neural network is not getting train or errors are not getting reduced. To reduce Neural Network errors, what we have to do with Neural Network architecture. These types of techniques are discussed and also discussed experiment and result. To solve different problems a neural network should be trained to perform correct classification..