On the iterative inversion of RBF networks: a statistical interpretation

The method of the inversion of arbitrary continuous multilayer networks is extended to the class of radial basis function networks. The problem centres around how to obtain an input pattern (or set of patterns) which has an associated network output which is close (in a least squares sense) to a desired target pattern. Why this nonlinear inverse problem is of interest is discussed and the action of inverting a radial basis function network is motivated from the point of view of statistical semiparametric density function estimation