Distortion-Invariant Associative Memories And Processors

Abstract

Associative memories represent a major new artificial intelligence type of processor. We consider their use in pattern recognition, with particular attention to distortion-invariant and adaptive pattern recognition. New associative memory techniques and pattern recognition oriented architectures suitable for multi-class distortion-invariant pattern recognition (including systems that provide adaptive updating, forgetting, achieve reduced dynamic range and improved performance) are discussed and initial results presented. The first results of distortion-invariance, multi-class associative memories for pattern recognition are presented together with new architectures and algorithms for multi-stage associative processors, iterative processors for associative memory synthesis, and multi-class distortion-invariant associative processors. The issue of orthogonal projection vectors, associative memory capacity and new results and techniques to synthesize associative memories are included throughout.