Abstract
The prediction of chaotic time series is a vital problem in nonlinear dynamical system. Radial Basis Function Neural Network (RBFNN) has been widely adopted in nonlinear dynamical system identification because of its simple topological structure, fast learning and strong extrapolating capability. The major problem in applying RBFNN is the selection of the number of hidden neurons. In this paper, we adopt the Localized Generalization Error Model (L-GEM) to select number of hidden neurons of RBFNN for chaotic time series prediction. The effectiveness of the L-GEM is evaluated by using two benchmarking chaotic time series datasets: Mackey-Glass series and Lorenz series. Simulations results show that the proposed method provides a better prediction performance in comparison with the RBFNN trained with a cross validation method.
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.
