Neural nets for adaptive filtering and adaptive pattern recognition

Abstract

The adaptive linear combiner (ALC) is described, and practical applications of the ALC in signal processing and pattern recognition are presented. Six signal processing examples are given, which are system modeling, statistical prediction, noise canceling, echo canceling, universe modeling, and channel equalization. Adaptive pattern recognition using neural nets is then discussed. The concept involves the use of an invariance net followed by a trainable classifier. It makes use of a multilayer adaptation algorithm that descrambles output and reproduces original patterns.<>