Abstract
A new algorithm of training neural networks by orthogonal weight functions (OWFs) is proposed, which is based on the training algorithm using cubic spline weight functions. The weights obtained after training are orthogonal functions defined on the sets of input variables (input patterns). Sensitivity analyses for neural networks using OWFs are also discussed in this paper. The sensitivity formulae of OWFs neural networks are derived. Based on the analyses of sensitivity, theoretical sensitivity and approximation sensitivity are also proposed. Finally, the correctness of the results proposed in this paper is verified by computational simulations.
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.
