Optimization of Hopfield Neural Network (HNN) using multiconnection and Lyapunov Energy Function (LEF) for storage and recall of handwritten images

Abstract

The HNN is often used as a human-like associative memory (AM). Nonetheless, HNNs suffered from limited noise tolerance and limited storage capacity. However, unfortunately, Hopfield neural networks suffered from limited tolerance to noise and limited storage capacity. This study proposes a novel approach for overcoming these limitations using the concept of multiple connections architecture in a Hopfield neural network with an energy function and Hamming distance (HD). In this architecture, a single connection among neurons of the network is employed to store a pattern, resulting in an etalon array, and the collection of etalon arrays corresponding to each neuronal connection forms a weight matrix (also called connection matrix). This approach of storing a single pattern on a single set of connections reduces the chances of being trapped in local minima and increases storage capacity because more patterns are stored in the network as the number of connections increases. The Lyapunov energy function and the Hamming distance concept are utilized to determine the testing pattern’s proximity to one of the stored patterns for perfect recall. Several experiments are conducted to compare the recall success rate and storage capacity of handwritten character images to those of existing methods. Experiments using multiple connections Hopfield neural network with hamming distance and Lyapunov energy function demonstrate promising results in comparison to existing methods.