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Data based constructive identification -- overcoming the curse of dimensionality

Data based constructive identification -- overcoming the curse of dimensionality
Data based constructive identification -- overcoming the curse of dimensionality
The modelling of a nonlinear stochastic dynamical processes from data involves solving the problems of data gathering, preprocessing, model architecture selection, learning or adaptation, parametric evaluation and model validation. For a given model architecture such as associative memory networks, a common problem in non-linear modelling is the problem of "the curse of dimensionality". A series of complementary data based constructive identification schemes, mainly based on but not limited to an operating point dependent fuzzy models, are introduced in this paper with the aim to overcome the curse of dimensionality. These include (i) a mixture of experts algorithm based on a forward constrained regression algorithm; (ii) an inherent parsimonious delaunay input space partition based piecewise local lineal modelling concept; (iii) a neurofuzzy model constructive approach based on forward orthogonal least squares and optimal experimental design and finally (iv) the neurofuzzy model construction algorithm based on basis functions that are Bézier Bernstein polynomial functions and the additive decomposition. Illustrative examples demonstrate their applicability, showing that the final major hurdle in data based modelling has almost been removed.
1-12
Harris, C. J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C. J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03

Harris, C. J. and Hong, X. (2000) Data based constructive identification -- overcoming the curse of dimensionality. pp. 1-12 .

Record type: Conference or Workshop Item (Other)

Abstract

The modelling of a nonlinear stochastic dynamical processes from data involves solving the problems of data gathering, preprocessing, model architecture selection, learning or adaptation, parametric evaluation and model validation. For a given model architecture such as associative memory networks, a common problem in non-linear modelling is the problem of "the curse of dimensionality". A series of complementary data based constructive identification schemes, mainly based on but not limited to an operating point dependent fuzzy models, are introduced in this paper with the aim to overcome the curse of dimensionality. These include (i) a mixture of experts algorithm based on a forward constrained regression algorithm; (ii) an inherent parsimonious delaunay input space partition based piecewise local lineal modelling concept; (iii) a neurofuzzy model constructive approach based on forward orthogonal least squares and optimal experimental design and finally (iv) the neurofuzzy model construction algorithm based on basis functions that are Bézier Bernstein polynomial functions and the additive decomposition. Illustrative examples demonstrate their applicability, showing that the final major hurdle in data based modelling has almost been removed.

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More information

Published date: 2000
Additional Information: Organisation: Plenary paper, IFAC AIRTC symposium Budapest October 2000
Organisations: Southampton Wireless Group

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Local EPrints ID: 254130
URI: http://eprints.soton.ac.uk/id/eprint/254130
PURE UUID: 052551dc-a196-4b86-90ab-74aaa0c128de

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Date deposited: 06 Jul 2001
Last modified: 18 Jul 2017 09:54

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