In this paper, a perceptron-based algorithm for fusion of multiple fingerprint matchers is presented. The person to be identified submits to the personal authentication system her/his fingerprint and claimed identity. Multiple fingerprint matchers provide a set of verification scores, that are then fused by a single-layer perceptron with class-separation loss function. Weights of such perceptro...

Although the multi-layer perceptron (MLP) neural networks provide a lot of flexibility and have proven useful reliable in wide range classification regression problems, they still limitations. One most common is associated with optimization algorithm used to train them. The commonly training method stochastic gradient descent backpropagation (or for short) because it mathematically tractable (g...

A multilayer perceptron is a feed forward artificial neural network model that maps sets of input data onto a set of appropriate output. It is a modification of the standard linear perceptron in that it uses three or more layers of neurons (nodes) with nonlinear activation functions and is more powerful than the perceptron in that it can distinguish data that is not linearly separable, or separ...

strip tearing during cold rolling process has always been considered among the main concerns for steel companies. several works have been done so far regarding the examination of the issue. in this paper, experimental data from cold rolling tandem mill is used for detecting strip tearing. sensors are placed across the cold rolling tandem mill. they receive information on parameters (such as ang...

no unique method has been so far specified for determining the number of neurons in hidden layers of multi-layer perceptron (mlp) neural networks used for prediction. the present research is intended to optimize the number of neurons using two meta-heuristic procedures namely genetic and hill climbing algorithms. the data used in the present research for prediction are consumption data of water...

In this paper, we will consider the problem of classifying electroencephalogram (EEG) signals of normal subjects, and subjects suffering from psychiatric disorder, e.g., obsessive compulsive disorder, schizophrenia, using a class of artificial neural networks, viz., multi-layer perceptron. It is shown that the multilayer perceptron is capable of classifying unseen test EEG signals to a high deg...