Fuzzy associative memories with autoencoding mechanisms

Associative memories constructed and operating in the presence of big data offer an effective way to realize association mechanisms aimed at storing and recalling items. In this study, we develop a logic-driven model of two-level fuzzy associative memories augmented by autoencoding processing. It is composed of two functional modules. The first module of this architecture implements an efficient dimensionality reduction of the original high dimensional data with the use of an autoencoder. This helps achieve storing and completing the recall realized by a logic-oriented associative memory which constitutes the second module of the architecture. The optimization of the association matrices studied in the paper involves both gradient-based learning mechanisms and the algorithms of population-based optimization, i.e., particle swarm optimization (PSO) and differential evolution (DE). A suite of experimental studies is presented to quantify the performance of the proposed approach. Comparative studies are also conducted to show and quantify the advantages of the mechanisms of associative recall and storage augmented by the autoencoding process.