Title :
The MI-RBFN: mapping for generalization
Author :
Deignan, Paul B., Jr. ; Meckl, Peter H. ; Franchek, Matthew A.
Author_Institution :
Sch. of Mech. Eng., Purdue Univ., West Lafayette, IN, USA
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.
Keywords :
estimation theory; generalisation (artificial intelligence); information theory; probability; radial basis function networks; Gaussian clustering; MI-RBFN; binning interval; complex continuous deterministic systems; expectation-maximization algorithm; generalization; incomplete information; mapping; mutual information-radial basis function network; Clustering algorithms; Convergence; Explosions; Intelligent networks; Mechanical engineering; Mutual information; Neural networks; Radial basis function networks; Testing; Yield estimation;
Conference_Titel :
American Control Conference, 2002. Proceedings of the 2002
Print_ISBN :
0-7803-7298-0
DOI :
10.1109/ACC.2002.1024527
