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Detail of publication
Citation
: Recursive parameters estimation and structure adaptation of neural network . Proceedings of the eighth IASTED international conference on Intelligent systems and control, p. 78-83, ACTA Press, Anaheim, 2005.
Abstract
Application of neural networks in identification of nonlin- ear stochastic systems is treated. The stress is laid on a parameters estimation and structure adaptation of the net- works. They are trained by a global filtering method allow- ing to determine conditional probability density functions of network parameters. The Gaussian sum approach used for parameters estimation of network gives better results than the commonly used prediction error methods, and it is an interesting alternative to sequential Monte Carlo meth- ods. The approach also enables structure adaptation which is given by pruning of insignificant connections from an a priori chosen large network. The designed structure adap- tation method utilizes conditional probability density func- tions of the parameters obtained from the estimation algo- rithm to measure saliency of the network connections and it represents a generalization of the extended Kalman filter based pruning method.
Detail of publication
| Title: | Recursive parameters estimation and structure adaptation of neural network |
|---|---|
| Author: | Šimandl, M. ; Hering, P. |
| Language: | English |
| Date of publication: | 31 Oct 2005 |
| Year: | 2005 |
| Type of publication: | Papers in proceedings of reviewed conferences |
| Title of journal or book: | Proceedings of the eighth IASTED international conference on Intelligent systems and control |
| Page: | 78 - 83 |
| ISBN: | 0-88986-517-5 |
| Publisher: | ACTA Press |
| Address: | Anaheim |
| Date: | 31 Oct 2005 - 2 Nov 2005 |
Keywords
system identification, nonlinear parameters estimation, structure adaptation, probability density function, multi- layer perceptron network.
BibTeX
@INPROCEEDINGS{SimandlM_2005_Recursiveparameters,
author = {\v{S}imandl, M. and Hering, P.},
title = {Recursive parameters estimation and structure adaptation of neural network},
year = {2005},
publisher = {ACTA Press},
journal = {Proceedings of the eighth IASTED international conference on Intelligent systems and control},
address = {Anaheim},
pages = {78-83},
ISBN = {0-88986-517-5},
url = {http://www.kky.zcu.cz/en/publications/SimandlM_2005_Recursiveparameters},
}