A recurrent perceptron learning algorithm for cellular neural networks

Abstract

A supervised learning algorithm for obtaining the template coefficients in completely stable Cellular Neural Networks (CNNs) is analysed in the paper. The considered algorithm resembles the well-known perceptron learning algorithm and hence called as Recurrent Perceptron Learning Algorithm (RPLA) when applied to a dynamical network. The RPLA learns pointwise defined algebraic mappings from initial-state and input spaces into steady-state output space; despite learning whole trajectories through desired equilibrium points. The RPLA has been used for training CNNs to perform some image processing tasks and found to be successful in binary image processing. The edge detection templates found by RPLA have performances comparable to those of Canny’s edge detector for binary images.