Abstract
In order to compare the capabilities of the Random Neural Network (RNN), a new neural network model introduced by Gelenbe (1989), with those of other models in associative memory applications, we examine here its storage capacity. The theoretical capacity is first derived for the single-layer fully-connected RNN and then for the three-layer N-M-N architecture. Experiences have been performed on single-layer networks of different size using RPROP learning algorithm (Riedmiller, 1992) to evaluate the corresponding critical storage capacity.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
