Efficiency improvements for fuzzy associative memory

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 contents of each pattern, and associations of patterns. Standard FAMs capture either contents or associations of patterns well, but not both of them. In this paper, we propose a novel FAM that effectively stores both contents and associations of patterns. We improve both learning and recalling processes of FAM. In learning process, the associations and contents are stored by mean of input and output patterns and they are generalised by erosion operator. In recalling process, a new threshold is added to output function to improve outputs. Experiments show that noise tolerance of the proposed FAM is better than standard FAMs with different types of noise.