Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - Evolving fuzzy rule-based systems for modelling of non-linear non-stationary processes
AU - Angelov, Plamen
PY - 2006/10/2
Y1 - 2006/10/2
N2 - 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
AB - 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
KW - fuzzy rule-based models
KW - evolving fuzzy systems
KW - on-line system identification
KW - Kalman filter
KW - recursive least squares
KW - clustering
U2 - 10.3182/20061002-4-BG-4905.00008
DO - 10.3182/20061002-4-BG-4905.00008
M3 - Conference contribution/Paper
SN - 978-3-902661-19-7
SP - 43
EP - 50
BT - IFAC Workshop on Energy Saving Control in Plants and Buildings (2006)
A2 - Erbe, Heinz-Hermann
A2 - Nikolov, Emil K.
PB - IFAC
T2 - IFAC Workshop on Energy Efficient Control
Y2 - 2 October 2006 through 5 October 2006
ER -