Abstract
Several learning algorithms have been derived for equilibrium points in recurrent neural networks. In this paper, we also consider learning the equilibrium points of such dynamical systems. We derive a structurally simple learning algorithm for recurrent networks which does not involve computing the trajectories of the system and we prove convergence and give examples. We also discuss solving for the connection weight matrix by iterative learning algorithms or direct solving.
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.
