Neurofuzzy Model Construction for the Modelling of Non-linear Processes


Bossley, K.M., Brown, M. and Harris, C.J. (1995) Neurofuzzy Model Construction for the Modelling of Non-linear Processes. 3rd European Control conference , 2438--2443.

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Description/Abstract

Neurofuzzy systems are ideal for modelling nonlinear processes; combining the transparent knowledge representation of fuzzy systems with the well established learning techniques of associative memory networks. In the control community there is a need for high-dimensional modelling, but unfortunately these neurofuzzy systems suffer from the curse of dimensionality. To make the use of high-dimensional neurofuzzy systems practical, off-line construction algorithms need to be developed which generate parsimonious models. This need for off-line construction algorithms is enhanced by the lack of expert knowledge from which models are traditionally developed. This paper outlines the concepts of B-spline neurofuzzy systems and presents several representations which can be exploited to produce parsimonious models. Finally, an off-line construction algorithm based on several of these representations is illustrated with a simple example.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Address: Rome, Italy
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
ePrint ID: 250234
Date Deposited: 04 May 1999
Last Modified: 27 Mar 2014 19:51
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/250234

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