An effective associative memory for pattern recognition

TL;DR

This paper introduces a new associative memory model that significantly improves storage capacity and noise immunity, especially effective for recognizing correlated patterns, advancing pattern recognition technology.

Contribution

The paper presents a novel associative memory model with enhanced storage and recognition capabilities, particularly for correlated patterns, surpassing existing models.

Findings

High storage capacity achieved

Improved noise immunity demonstrated

Effective recognition of correlated patterns

Abstract

Neuron models of associative memory provide a new and prospective technology for reliable date storage and patterns recognition. However, even when the patterns are uncorrelated, the efficiency of most known models of associative memory is low. We developed a new version of associative memory with record characteristics of its storage capacity and noise immunity, which, in addition, is effective when recognizing correlated patterns.