Using Genetic Algorithms for Second-Order Cellular Neural Networks for Edge Detection

In this part of the article, we focus on building a learning algorithm using genetic algorithms (GA) for Second-Order Cellular Neural Networks (SOCeNNs). We used supervised learning with a set of input sample images to determine the optimal set of weights for a second-order cellular neural network. From that result, evaluate the applicability of SOCeNNs for the problem of object edge detection in image processing.