CNN Learning for Image Processing: Center of Mass versus Genetic Algorithms

Abstract

This paper presents a comparative performance analysis of two learning algorithms developed for the use in Cellular Neural Networks (CNN): the Center of Mass Algorithm, a back-propagation like technique, and an adaptation of the Genetic Algorithm. Both methods are applied for the training of a CNN built with Full Signal Range (FSR) cells, for the implementation of several well-known bipolar functions of image processing. Performance parameters such as total execution time, number of CNN runs and success rate are assessed in order to provide guidelines for the learning method choice.