Performance Comparison Between Self-Organizing Map and Generalized Feed Forward Neural Network

摘要

In this study, we compare the performance of well-known neural networks, namely, Self-organizing map neural network and generalized feed- forward neural network An artificial neural network (ANN) is an information-processing paradigm inspired by the way the densely interconnected, parallel structure of the mammalian brain processes information. The key element of the ANN paradigm is the novel structure of the information processing system. Learning in ANN typically occurs by example through training, or exposure to a set of input/output data where the training algorithm iteratively adjusts the connection weights. The Kohonen self-organizing map neural network performs a mapping from a continuous input space to a discrete output space, preserving the topological properties of the input. In a fully connected multilayer generalized feedforward network, each neuron in one layer is connected by a weight to every neuron in the layer downstream it. The back propagation algorithm is used for training. For training and testing the neural network various databases available on the Internet as well as gathered information from hospitals is used.