Fast Classification Networks For Signal Processing

Abstract

We present a generalization of the corner classification approach to training feed-forward neural networks that allows rapid learning of nonbinary data. These generalized networks, called fast classification (FC) networks, are compared against backpropagation and radial basis function networks and are shown to have excellent performance for prediction of time series and pattern recognition. FC networks do not require iterative training and they can be used in many signal processing applications where fast, nonlinear filtering provides an advantage.