Parallel distributed processing: practical applications of neural networks in signal processing

Abstract

An introduction to artificial neural network models is presented, along with an overview of their practical application and potential applications in signal processing. Successful neural network implementations are described and their performances are compared to those of more traditional signal processing implementations. The Hopfield net, self-organizing feature maps, and the multilayer perceptron are reviewed. Implementation of neural nets in speech synthesis, speech recognition, target identification, image processing, pattern matching, error-correction coding, and neurocomputing are reported. Several ICs in production are briefly mentioned.<>