Abstract
Comparison with the classical BP neural network, the generalized regression neural network requires not periodic training process but a smoothing parameter. The model has steady and fast speed, and meanwhile, the connection weight of different neurons is not necessary to be adjusted in the training process. The paper establishes the index system of GRNN forecasting model, and then uses Bayes theory to reduce them, which will be inputting variables of GRNN model. The method is testified to get higher speed and accuracy by simulation of actual data and comparison to classical BP neural network.
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.
