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Non-linear systems identification using radial basis functions

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
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 System Science, 21 (12), 2513-2539. (doi:10.1080/00207729008910567).

Record type: Article

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

Identifiers

Local EPrints ID: 454135
URI: http://eprints.soton.ac.uk/id/eprint/454135
PURE UUID: 12f552c5-212e-4cc5-9ee6-b466dd21af6c

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Date deposited: 01 Feb 2022 17:38
Last modified: 03 Feb 2022 17:41

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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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