An Enhanced Learning for Restricted Hopfield Networks

dc.contributor.authorHalabian, Faezeh
dc.description.abstractThis research investigates developing a training method for Restricted Hopfield Network (RHN) which is a subcategory of Hopfield Networks. Hopfield Networks are recurrent neural networks proposed in 1982 by John Hopfield. They are useful for different applications such as pattern restoration, pattern completion/generalization, and pattern association. In this study, we propose an enhanced training method for RHN which not only improves the convergence of the training sub-routine, but also is shown to enhance the learning capability of the network. Particularly, after describing the architecture/components of the model, we propose a modified variant of SPSA which in conjunction with back-propagation over time result in a training algorithm with an enhanced convergence for RHN. The trained network is also shown to achieve a better memory recall in the presence of noisy/distorted input. We perform several experiments, using various datasets, to verify the convergence of the training sub-routine, evaluate the impact of different parameters of the model, and compare the performance of the trained RHN in recreating distorted input patterns compared to conventional RBM and Hopfield network and other training methods.
dc.publisherUniversité d'Ottawa / University of Ottawa
dc.subjectRestricted Hopfield Networks
dc.subjectRecurrent Neural Network
dc.subjectSPSA training algorithm
dc.subjectBack propagation through time
dc.subjectAssociative memory
dc.titleAn Enhanced Learning for Restricted Hopfield Networks
dc.contributor.supervisorKiringa, Iluju
dc.contributor.supervisorYeap, Teténie / Engineering
uottawa.departmentScience informatique et génie électrique / Electrical Engineering and Computer Science
CollectionThèses, 2011 - // Theses, 2011 -