Non-linear systems identification using radial basis functions
Non-linear systems identification using radial basis functions
This paper investigates the identification of discrete-time non-linear systems using radial basis functions. A forward regression algorithm based on an orthogonal decomposition of the regression matrix is employed to select a suitable set of radial basis function centers from a large number of possible candidates and this provides, for the first time, fully automatic selection procedure for identifying parsimonious radial basis function models of structure-unknown non-linear systems. The relationship between neural networks and radial basis functions is discussed and the application of the algorithms to real data is included to demonstrate the effectiveness of this approach.
2513-2539
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Billings, S.A
36bfecd2-c9e2-4ee6-98dc-e1277c1c68e7
Cowan, C.F.N.
d1dce4b7-a715-4ada-beb5-ea9e732f930a
Grant, P.M.
eedba4d3-5e35-446b-bf5d-34dc76cff3b8
1 December 1990
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Billings, S.A
36bfecd2-c9e2-4ee6-98dc-e1277c1c68e7
Cowan, C.F.N.
d1dce4b7-a715-4ada-beb5-ea9e732f930a
Grant, P.M.
eedba4d3-5e35-446b-bf5d-34dc76cff3b8
Chen, Sheng, Billings, S.A, Cowan, C.F.N. and Grant, P.M.
(1990)
Non-linear systems identification using radial basis functions.
International Journal of Systems Science, 21 (12), .
(doi:10.1080/00207729008910567).
Abstract
This paper investigates the identification of discrete-time non-linear systems using radial basis functions. A forward regression algorithm based on an orthogonal decomposition of the regression matrix is employed to select a suitable set of radial basis function centers from a large number of possible candidates and this provides, for the first time, fully automatic selection procedure for identifying parsimonious radial basis function models of structure-unknown non-linear systems. The relationship between neural networks and radial basis functions is discussed and the application of the algorithms to real data is included to demonstrate the effectiveness of this approach.
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Published date: 1 December 1990
Additional Information:
Address: London
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 251140
URI: http://eprints.soton.ac.uk/id/eprint/251140
ISSN: 0020-7721
PURE UUID: 89096a1b-33f7-498b-9e75-7ddb1f9fd90e
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Date deposited: 12 Oct 1999
Last modified: 14 Mar 2024 05:09
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Contributors
Author:
Sheng Chen
Author:
S.A Billings
Author:
C.F.N. Cowan
Author:
P.M. Grant
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