title: A comparison of the von Mises and Gaussian basis functions for approximating spherical acoustic scatter.
abstract: This paper compares the approximation accuracy of two basis functions that share a common radial basis function (RBF) neural network architecture used for approximating a known function on the unit sphere. The basis function types considered are that of a new spherical basis function, the von Mises function, and the now well-known Gaussian basis function. Gradient descent learning rules were applied to optimize (learn) the solution for both approximating basis functions. A benchmark approximation problem was used to compare the performance of the two types of basis functions, in this case the mathematical expression for the scattering of an acoustic wave striking a rigid sphere.
authors.affiliation: Dept. of Psychol., Wisconsin Univ., Madison, WI.
authors.forename: R L
authors.initials: RL
authors.lastname: Jenison
authors.forename: K
authors.initials: K
authors.lastname: Fissell