Abstract
In order to improve the training speed of multilayer feedforward neural networks, we proposed and explored fast backpropagation (BP) algorithms by introducing the hybrid global optimization conjugate gradient algorithm for the dynamic learning rate. This was to overcome the BP learning problem which caused plunging into local minima or slow convergence. Our algorithm is of a higher recognition rate than that of the Polak-Ribieve conjugate gradient and conventional BP algorithms. It showed less training time, less complication and stronger robustness than the Fletcher-Reeves conjugate gradient and conventional BP algorithms for real speech data.
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.
