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

Research output: Contribution in Book/Report/ProceedingsPaper

Published

Publication date2/10/2006
Host publicationIFAC Workshop on Energy Saving Control in Plants and Buildings (2006)
EditorsHeinz-Hermann Erbe, Emil K. Nikolov
PublisherIFAC
Pages43-50
Number of pages8
ISBN (Print)978-3-902661-19-7
Original languageEnglish

Conference

ConferenceIFAC Workshop on Energy Efficient Control
CityBansko, Bulgaria
Period2/10/065/10/06

Conference

ConferenceIFAC Workshop on Energy Efficient Control
CityBansko, Bulgaria
Period2/10/065/10/06

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