Singular value Decomposition applied to Associative Memory of Hopfield Neural Network

Abstract

Content-addressable memories are useful for storage and data retrieval from arrays of sensors. The Hopfield neural network is a model of associative content-addressable memory with a simple flexible structure. Design of this artificial neural network is capable of memorizing large quantity of data and recalling the same from available information. In this work, we have shown that with corrupted input dataset, the correct set of data can be retrieved from approximated or compressed associative memory matrix. The idea has been explained through an experiment using binary datasets. The proposed methodology will increase the storage capacity of associative memory. In the experiment we have shown that it will minimize the number of arithmetic operations involved in recovery process of the Hopfield model but will also ensure correctness of information retrieval from the synaptic matrix from a corrupted dataset as input.