DocumentCode
1817841
Title
Piecewise linear networks (PLN) for function approximation
Author
Eppler, Wolfgang ; Beck, Hans N.
Author_Institution
Forschungszentrum Karlsruhe, Germany
Volume
1
fYear
1999
fDate
1999
Firstpage
388
Abstract
A piecewise linear network is a general neural network with three layers. It was designed for fast function approximation with a good generalization capability even in the case of very few data points. An intuitive understanding of the network processing is possible and the complexity of the network varies with the complexity of the function being approximated. This means that strong nonlinear functions are modelled by networks with more complex structure than the linear ones. The training of the network is constructive. The user provides only one parameter for the algorithm: the abort condition, when the training of the network should stop
Keywords
computational complexity; feedforward neural nets; function approximation; generalisation (artificial intelligence); learning (artificial intelligence); computational complexity; function approximation; generalization; learning; multilayer neural network; nonlinear functions; piecewise linear network; Control system synthesis; Control systems; Function approximation; Neural networks; Neurons; Nonlinear equations; Piecewise linear approximation; Piecewise linear techniques; System identification; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-5529-6
Type
conf
DOI
10.1109/IJCNN.1999.831525
Filename
831525
Link To Document