In this paper we address the problem of optimal parameter selection for a Multilayer Perceptron by means of a neural network with only one hidden layer that uses the "back propagation" algorithm over relatively simple classification problems in two dimensions (input patterns with only two variables). We will show graphically the direct relation existing between the increasing complexity regions (classes) and the necessity to add more neurons in the hidden layer. At the end, we summarize our findings by means of parameter selection recommendations in order to avoid the tedious and blind "trial and error" method.
Toward Optimal Parameter Selection for the Multi-layer Perceptron Artificial Neural Network
2013-11-01
470903 byte
Conference paper
Electronic Resource
English
Automatic Incident Detection on Freeways Using Multi-Layer Perceptron Neural Network
British Library Conference Proceedings | 2002
|Multi-Layer Perceptron Based Lung Tumor Classification
IEEE | 2018
|