Article Fingerprint
ReserarchID
6W1ZI
Crossref Journal DOI 10.17406/gjre
Print ISSN 0975-5861
e-ISSN 2249-4596
Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.
Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.
This paper attempted to assess the attitudes of students in
Advances in technology have created the potential for a new
Inclusion has become a priority on the global educational agenda,
A trainable analog restricted Hopfield Network is presented in this paper. It consists of two layers of nodes, visible and hidden nodes, connected by weighted directional paths forming a bipartite graph with no intralayer connection. An energy or Lyapunov function was derived to show that the proposed network will converge to stable states. The proposed network can be trained using either the modified SPSA or BPTT algorithms to ensure that all the weights are symmetric. Simulation results show that the presence of hidden nodes increases the network’s memory capacity. Using EXOR as an example, the network can be trained to be a dynamic classifier. Using A, U, T, S as training characters, the network was trained to be an associative memory. Simulation results show that the network can perform perfect re-creation of noisy images. Its recreation performance has higher noise tolerance than the standard Hopfield Network and the Restricted Boltzmann Machine. Simulation results also illustrate the importance of feedback iteration in implementing associative memory to re-create from noisy images.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
