Open AccessLearning Behaviors of Stochastic Gradient Radial Basis Function Network Algorithms for Odor Sensing Systems
Author(s) -
Kim Namyong,
Byun HyungGi,
Kwon Ki Hyeon
Publication year - 2006
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.06.0105.0046
Subject(s) - radial basis function , convergence (economics) , singular value decomposition , algorithm , radial basis function network , computer science , variance (accounting) , function (biology) , artificial neural network , gradient descent , artificial intelligence , mathematics , pattern recognition (psychology) , business , accounting , evolutionary biology , economics , biology , economic growth
Learning behaviors of a radial basis function network (RBFN) using a singular value decomposition (SVD) and stochastic gradient (SG) algorithm, together named RBF‐SVD‐SG, for odor sensing systems are analyzed, and a fast training method is proposed. RBF input data is from a conducting polymer sensor array. It is revealed in this paper that the SG algorithm for the fine‐tuning of centers and widths still shows ill‐behaving learning results when a sufficiently small convergence coefficient is not used. Since the tuning of centers in RBFN plays a dominant role in the performance of RBFN odor sensing systems, our analysis is focused on the center‐gradient variance of the RBFN‐SVD‐SG algorithm. We found analytically that the steady‐state weight fluctuation and large values of a convergence coefficient can lead to an increase in variance of the center‐gradient estimate. Based on this analysis, we propose to use the least mean square algorithm instead of SVD in adjusting the weight for stable steady‐state weight behavior. Experimental results of the proposed algorithm have shown faster learning speed and better classification performance.