A COMPARATIVE STUDY OF GSN^f LEARNING METHODS

GSN^f is a Boolean Neural Network designed to be applied for pattern recognition tasks. This chapter presents the learning algorithms which have been proposed to train GSN^f architectures and compare their performances as key parameters are changed. These algorithms are evaluated against each other by taking into account training time, saturation, learning conflicts and correct recognition rates.