Abstract

An improved algorithm using B-splines as weight functions for training neural networks is proposed. There is no need for training neural networks or solving linear equations. The most important advantage is that we can get the forms of weight functions by the given patterns directly. Each of weight function is a one-variable function and takes one associated input point (input neuron) as its argument. The form of each weight function is a linear combination of some B-splines defined on the sets of given input variables (input knots or input patterns), whose coefficients are associated with the given output patterns. Some examples are presented to illustrate good performance of the new algorithm.

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 call

Disclaimer: 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.