The mutual information - radial basis function network (MI-RBFN) is an efficient, general, and integrated method of approximating complex, continuous, deterministic systems from incomplete information. The nodes of the MI-RBFN are located by clustering local mutual information estimates thereby yielding a mapping that inherently generalizes better than one formulated by seeking solely to minimize residuals. The expectation-maximization algorithm is introduced for Gaussian clustering of MI estimates. A further improvement in the methodology is marked by the specification of a set of rules for intelligently determining the binning interval of the input and target spaces.
ASJC Scopus subject areas
- Electrical and Electronic Engineering