Abstract
This paper deals with a fast and computationally simple Successive Over-relaxation Resilient Backpropagation (SORRPROP) learning algorithm which has been developed by modifying the Resilient Backpropagation (RPROP) algorithm. It uses latest computed values of weights between the hidden and output layers to update remaining weights. The modification does not add any extra computation in RPROP algorithm and maintains its computational simplicity. Classification and regression simulations examples have been used to compare the performance. From the test results for the examples undertaken it is concluded that SORRPROP has small convergence times and better performance in comparison to other first-order learning algorithms.
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.
