Abstract
Differential evolution (DE) is an exceptionally fast and robust population based search algorithm that is able to locate near optimal solutions to difficult problems. Beside its good convergence properties, DE is very simple to understand and to implement. This paper describes an evolutionary neural network-training algorithm for beta basis function neural networks (BBFNN) using DE. Application to function approximation problems are considered to demonstrate the performance of the BBFNN and of the evolutionary algorithm.
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.
