title: Multiplication-free radial basis function network.
abstract: For the purpose of adaptive function approximation, a new radial basis function network is proposed which is nonlinear in its parameters. The goal is to reduce significantly the computational effort for a serial processor, by avoiding multiplication in both the evaluation of the function model and the computation of the parameter adaptation. The approximation scheme makes use of a grid-based Gaussian basis function network. Due to the local support of digitally implemented Gaussian functions the function representation is parametric local and therefore well suited for an online implementation on a microcomputer. A gradient descent based nonlinear learning algorithm is presented and the convergence of the algorithm is proved.
authors.affiliation: Inst. fur Allgemeine Elekrotechnik-Automobilelektronik, Vienna Univ. of Technol., Vienna.
authors.forename: M
authors.initials: M
authors.lastname: Heiss
authors.forename: S
authors.initials: S
authors.lastname: Kampl