Morphological perceptrons

Single-layer and multilayer perceptrons are the simplest type of feedforward neural network systems. The first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Application of a nonlinear activation function follows the linear operation in order to provide for nonlinearity of the network and set the next state of the neuron. We introduce a novel class of perceptrons, called morphological perceptrons, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological neuron computation is nonlinear before the application of a nonlinear activation function. As a consequence, the properties of morphological perceptrons are very different to those of traditional feedforward models.