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Evolving fuzzy rule-based systems for modelling of non-linear non-stationary processes

Angelov, Plamen (2006) Evolving fuzzy rule-based systems for modelling of non-linear non-stationary processes. In: IFAC Workshop on Energy Saving Control in Plants and Buildings (2006). IFAC, pp. 43-50. ISBN 978-3-902661-19-7

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Abstract

Recently introduced concept of evolving fuzzy modeling will be reviewed in this paper in the context of non-linear on-line modeling and identification of non-stationary processes. It will be presented as a logical consequence of the attempts to cope with the non-stationarity, non-linearity and the requirements for practical on-line algorithms that work in real-time and are close to the theoretically optimal/analytical solutions. The basic elements of the concept and its procedure will be outlined. This concept will be presented as a higher level adaptation that concerns model structure as well as model parameters. It will be presented as an extension of the multi-model concept and of the on-line identification of fixed structure fuzzy rule-based models. The name 'evolving' will be justified by the features of the algorithm such as 'inheritance', 'gradual change', 'learning by experience', 'self-organization', and 'age' that are typical for the evolution of living individuals and especially humans. One should distinguish this from the 'evolutionary computation' algorithms also called 'genetic' algorithms where the evolution of population of individuals is mimicked and operations such as 'crossover', 'mutation', 'reproduction' and 'selection' are used. From a pragmatic point of view, the new paradigm of 'evolving systems' is closer to on-line, real-time identification and adaptive systems branches of control theory while 'evolutionary algorithms' are heuristics which is firmly in the field of artificial intelligence. A number of application of this technique to a range of industrial and benchmark processes has been recently reported. Due to the lack of space only one problem related to building energy will be presented, namely modeling a typical air-conditioning unit will be presented. (c) IFAC

Item Type: Contribution in Book/Report/Proceedings
Uncontrolled Keywords: fuzzy rule-based models ; evolving fuzzy systems ; on-line system identification ; Kalman filter ; recursive least squares ; clustering
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 56224
Deposited By: ep_importer_pure
Deposited On: 19 Jul 2012 17:18
Refereed?: No
Published?: Published
Last Modified: 21 Mar 2014 10:41
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/56224

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