A Neural Network for Real-Time Signal Processing

Abstract

This paper describes a neural network algorithm that (1) performs temporal pattern matching in real-time, (2) is trained on-line, with a single pass, (3) requires only a single template for training of each representative class, (4) is continuously adaptable to changes in background noise, (5) deals with transient signals having low signal(cid:173) to-noise ratios, (6) works in the presence of non-Gaussian noise, (7) makes use of context dependencies and (8) outputs Bayesian proba(cid:173) bility estimates. The algorithm has been adapted to the problem of passive sonar signal detection and classification. It runs on a Con(cid:173) nection Machine and correctly classifies, within 500 ms of onset, signals embedded in noise and subject to considerable uncertainty.