Improving learning rule for fuzzy associative memory with combination of content and association

Abstract

FAM is an associative memory that uses operators of fuzzy logic and mathematical morphology (MM). FAMs possess important advantages including noise tolerance, unlimited storage, and one pass convergence. An important property, deciding FAM performance, is the ability to capture content of each pattern, and association of patterns. Existing FAMs capture either content or association of patterns well, but not both of them. They are designed to handle either erosive or dilative noise in distorted inputs but not both. Therefore, they cannot recall distorted input patterns very well when both erosive and dilative noises are present. In this paper, we propose a new FAM called content-association associative memory (ACAM) that stores both content and association of patterns. The weight matrix is formed with the weighted sum of output pattern and the difference between input and output patterns. Our ACAM can handle inputs with both erosive and dilative noises better than existing models.