An introduction to morphological perceptrons with competitive learning

Morphological neural networks grew out of a merger of ideas from artificial neural networks and mathematical morphology. The morphological perceptron, one of the first morphological neural networks that appeared in the literature, was originally developed as a simple model for solving binary classification problems. The morphological perceptron has not received much attention due to its simplicity and limited applicability. In this paper, we introduce a new version of the morphological perceptron called morphological perceptron with competitive learning including an appropriate algorithm for training this model. Instead of a single binary output neuron like the original morphological perceptron, the new model as a winner-take-all output layer and the decision surface after training does not depend on the order in which the patterns are presented to the network. Finally, the paper includes some experimental results on two well-known datasets that indicate the utility of the morphological perceptron with competitive learning in classification problems.