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ŠKORPIL, V. ŠŤASTNÝ, J.
Original Title
Analysis of Algorithms for Radial Basis Function Neural Network. Springer Verlag:
Type
journal article in Web of Science
Language
English
Original Abstract
This paper describes the analysis of algorithms for the hidden layer construction of network and for learning of the Radial Basis Function neural Network (RBFN). We compared results obtained by using of learning algorithms LMS (Least Mean Square) and Gradient Algorithms (GA) and results are obtained by using of algorithms APC-III and K-means for hidden layer contruction of neural network. The principles and algorithms given below have been used in an application for object classification that was developed at Brno University of Technology. This solution is suitable for the research of personal wireless communications and similar systems.
Keywords
Radial basis function, Learning algorithm, Neuron, Hidden layer.
Authors
ŠKORPIL, V.; ŠŤASTNÝ, J.
RIV year
2007
Released
7. 9. 2007
Publisher
Springer
Location
Prague
ISBN
1571-5736
Periodical
Mobile and Wireless Communication Networks
Year of study
Number
1
State
United States of America
Pages from
54
Pages to
62
Pages count
9
BibTex
@article{BUT47411, author="Vladislav {Škorpil} and Jiří {Šťastný}", title="Analysis of Algorithms for Radial Basis Function Neural Network. Springer Verlag:", journal="Mobile and Wireless Communication Networks", year="2007", volume="2007", number="1", pages="54--62", issn="1571-5736" }