The MI - RBFN: Mapping for generalization

Paul B. Deignan, Peter H. Meckl, Matthew A. Franchek

Research output: Contribution to journalConference article

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)3840-3845
Number of pages6
JournalProceedings of the American Control Conference
Volume5
DOIs
StatePublished - 2002
Event2002 American Control Conference - Anchorage, AK, United States
Duration: May 8 2002May 10 2002

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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