TY - JOUR

T1 - Automatic generation of fuzzy rule-based models from data by genetic algorithms.

AU - Angelov, Plamen

AU - Buswell, Richard

N1 - The final, definitive version of this article has been published in the Journal, Information Sciences 150 (1-2), 2002, © ELSEVIER.

PY - 2003

Y1 - 2003

N2 - A methodology for the encoding of the chromosome of a genetic algorithm (GA) is described in the paper. The encoding procedure is applied to the problem of automatically generating fuzzy rule-based models from data. Models generated by this approach have much of the flexibility of black-box methods, such as neural networks. In addition, they implicitly express information about the process being modelled through the linguistic terms associated with the rules. They can be applied to problems that are too complex to model in a first principles sense and can reduce the computational overhead when compared to established first principles based models. The encoding mechanism allows the rule base structure and parameters of the fuzzy model to be estimated simultaneously from data. The principle advantage is the preservation of the linguistic concept without the need to consider the entire rule base. The GA searches for the optimum solution given a comparatively small number of rules compared to all possible. This minimises the computational demand of the model generation and allows problems with realistic dimensions to be considered. A further feature is that the rules are extracted from the data without the need to establish any information about the model structure a priori. The implementation of the algorithm is described and the approach is applied to the modelling of components of heating ventilating and air-conditioning systems. (c) Information Sciences

AB - A methodology for the encoding of the chromosome of a genetic algorithm (GA) is described in the paper. The encoding procedure is applied to the problem of automatically generating fuzzy rule-based models from data. Models generated by this approach have much of the flexibility of black-box methods, such as neural networks. In addition, they implicitly express information about the process being modelled through the linguistic terms associated with the rules. They can be applied to problems that are too complex to model in a first principles sense and can reduce the computational overhead when compared to established first principles based models. The encoding mechanism allows the rule base structure and parameters of the fuzzy model to be estimated simultaneously from data. The principle advantage is the preservation of the linguistic concept without the need to consider the entire rule base. The GA searches for the optimum solution given a comparatively small number of rules compared to all possible. This minimises the computational demand of the model generation and allows problems with realistic dimensions to be considered. A further feature is that the rules are extracted from the data without the need to establish any information about the model structure a priori. The implementation of the algorithm is described and the approach is applied to the modelling of components of heating ventilating and air-conditioning systems. (c) Information Sciences

KW - Fuzzy rule-based models

KW - Self learning

KW - Genetic algorithms

KW - Structure and parameter identification

U2 - 10.1016/S0020-0255(02)00367-5

DO - 10.1016/S0020-0255(02)00367-5

M3 - Journal article

VL - 150

SP - 17

EP - 31

JO - Information Sciences

JF - Information Sciences

SN - 0020-0255

IS - 1-2

ER -