Abstract
The chaotic time series forecast using Wavelet Neural Networks(WNN) was researched in this paper.An improved training method for WNN was presented.This method combines the Genetic Arithmetic(GA) and gradient descent BP method,and the BP method was embedded in the GA operation in order to resolve the GA's limitation in detail search capability.In the last step of this method the WNN trained by GA searches the best solution using BP method once again.The experiment on predicting the chaotic time series from Henon map validates the performance of the method in this paper;the experimental result also shows the method could assure the WNN convergence and have high forecasting precision.
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.
