Abstract
Lp approximation capability of radial basis function (RBF) neural networks is investigated. If g: R+1 → R1 and \( g(\parallel x\parallel _{R^n } ) \) ∈ Llocp(Rn) with 1 ≤ p < ∞, then the RBF neural networks with g as the activation function can approximate any given function in Lp(K) with any accuracy for any compact set K in Rn, if and only if g(x) is not an even polynomial.
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.
