Learning of RBF network models for prediction of unmeasured parameters by use of rules extraction algorithm

Author

Vachkov, G.L. ; Kiyota, Y. ; Komatsu, K.

Author_Institution

Dept. of Reliability-based Inf. Syst. Eng., Kagawa Univ., Takamatsu, Japan

fYear

2005

fDate

26-28 June 2005

Firstpage

292

Lastpage

297

Abstract

The paper presents three different methods for learning of normalized RBF network models that are similar in structure to the Takagi-Sugeno fuzzy models. These methods use different groups of parameters for optimization and incorporate a rules extraction algorithm for numerical evaluation of the connection weights, as a part of the optimization. Combinations of the methods give different learning strategies, which are analyzed in the paper through two simulated and one real example.

Keywords

knowledge acquisition; learning (artificial intelligence); optimisation; radial basis function networks; connection weight; learning strategy; normalized RBF network model; numerical evaluation; parameter optimization; radial basis function; rules extraction; unmeasured parameter prediction; Analytical models; Cities and towns; Data mining; Hardware; Information systems; Optimization methods; Predictive models; Radial basis function networks; Sensor phenomena and characterization; Systems engineering and theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American

Print_ISBN

0-7803-9187-X

Type

conf

DOI

10.1109/NAFIPS.2005.1548550

Filename

1548550