Neurofuzzy selfmade network for image processing based on CNN networks
José Antonio Medina Hernández, Felipe Gómez Castañeda, José Antonio Moreno Cadenas · 2011
The Cellular Neural Network (CNN) is very efficient for image processing tasks. However, there are limitations in its processing capabilities because some tasks can not be learned by a single CNN network. In recent years it has been accepted that a set of CNNs connected in parallel can realize more complex image processing tasks than a single CNN. Also recently it has been reported an architecture of neurofuzzy adaptable network (SIMAP) able to construct its structure and membership functions using only input-output data. In this paper is described the way of associating a CNN for every fuzzy rule in the SIMAP network for making complex image processing tasks, which are impossible to do for a unique CNN.