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Author Ciftcioglu, O. ♦ Sariyildiz, I.S.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2005
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Radial basis function networks ♦ Fuzzy systems ♦ Power system modeling ♦ Fuzzy sets ♦ Function approximation ♦ Buildings ♦ Chromium ♦ Artificial neural networks ♦ Fuzzy logic ♦ Neural networks
Abstract The selection of optimum membership functions and deduction of rules from observed data can be implicitly carried out using conventional artificial neural networks, which can adaptively adjust membership functions and fine-tune rules to achieve better performance. This has resulted in many neuro-fuzzy approaches. These approaches, however do not contribute to the development of fuzzy logic. Also, selection of the number of processing nodes, the number of layers, and the interconnections among these layers in neural networks, still lacks systematic procedures. The purpose of this paper is to provide a systematic procedure for the construction of data driven fuzzy models, getting support from a special type multivariable function approximation network, known as radial basis functions (RBF) network the normalized form of which becomes equivalent to a fuzzy model. The normalized RBF network nodes in the hidden layer plays important role according to the case they are involved. One of the key features of normalized RBF networks is their excellent generalization. This property can be exploited to reduce the number of hidden nodes in function representation and classification tasks. To this end, normalized RBF network can be exploited to reduce the number of fuzzy sets in a fuzzy model while the explosive growth in multivariable case is greatly alleviated. The implication of this is the enhanced transparency and accuracy of the fuzzy model. These issues are investigated and the outcomes are reported in this research.
Description Author affiliation: Fac. of Archit. Building Technol., Delft Univ. of Technol., Netherlands (Ciftcioglu, O.; Sariyildiz, I.S.)
ISBN 078039187X
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-06-26
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 4.65 MB
Page Count 6
Starting Page 156
Ending Page 161

Source: IEEE Xplore Digital Library