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A new identification algorithm for fuzzy relational models and its application in model-based control

Record type: Article

Fuzzy relational modelling is a 'grey-box' method of modelling complicated, non-linear, systems directly from input-output data. Conventional methods of relational model identification, which rely on arguments based on set theory, are very fast, but do not produce models with very high accuracy. Identification using direct search numerical optimisation is able to significantly increase model accuracy, but at the cost of greatly increased computation time. This paper describes a new method for fuzzy relational model identification which makes use of a particular form of relational model structure. The principal advantage is that it is linear in its parameters, allowing conventional linear least-squares techniques to be used to identify the model. The performance of the new technique is compared with previous methods of identification using the well established Box-Jenkins furnace data. The method is able to achieve a very similar performance to direct-search optimisation methods, but in a fraction of the time. By imbedding a model generated by the new technique in a model-based controller, and comparing the results with earlier work, it is also shown that the improved model accuracy greatly improves the controller performance.

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Citation

Postlethwaite, B.E., Brown, M. and Sing, C.H. (1997) A new identification algorithm for fuzzy relational models and its application in model-based control Trans IChemE, 453--458.

More information

Published date: 1997
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 250119
URI: http://eprints.soton.ac.uk/id/eprint/250119
PURE UUID: b6fff113-e9e9-420f-910e-cb6f5e3ae289

Catalogue record

Date deposited: 04 May 1999
Last modified: 18 Jul 2017 10:43

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Contributors

Author: B.E. Postlethwaite
Author: M. Brown
Author: C.H. Sing

University divisions


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