DocumentCode
729368
Title
Extreme learning machine for function approximation - interval problem of input weights and biases
Author
Dudek, Grzegorz
Author_Institution
Dept. of Electr. Eng., Czestochowa Univ. of Technol., Czestochowa, Poland
fYear
2015
fDate
24-26 June 2015
Firstpage
62
Lastpage
67
Abstract
In this article the approximation capability of the extreme learning machine is studied. Specifically the impact of the range from which the input weights and biases are randomly generated on the fitted curve complexity is analyzed. The guidance for how to generate the input weights and biases to get good performance in approximation of the functions of one variable is provided.
Keywords
computational complexity; curve fitting; function approximation; learning (artificial intelligence); mathematics computing; extreme learning machine; fitted curve complexity; function approximation; interval problem; Complexity theory; Fitting; Function approximation; Neurons; Noise; Training; extreme learning machine; feedforward neural networks; function approximation;
fLanguage
English
Publisher
ieee
Conference_Titel
Cybernetics (CYBCONF), 2015 IEEE 2nd International Conference on
Conference_Location
Gdynia
Print_ISBN
978-1-4799-8320-9
Type
conf
DOI
10.1109/CYBConf.2015.7175907
Filename
7175907
Link To Document