Abstract
The use of knowledge-based systems can represent an efficient approach for system management, providing automatic control strategies with Artificial Intelligence capabilities. By means of Artificial Intelligence, the system is capable of assessing, diagnosing and suggesting the best operation mode. One important Artificial Intelligence tool for automatic control is the use of fuzzy logic controllers, which are fuzzy rule-based systems comprising the expert knowledge in form of linguistic rules. These rules are usually constructed by an expert in the field of interest who can link the facts with conclusions. However, this way to work sometimes fails to obtain an optimal behavior. To solve this problem, within the framework of Machine Learning, some artificial intelligence techniques could be applied to enhance the controller behavior.
In this work, a post-processing method is used to obtain more compact and accurate fuzzy logic controllers. This method combines a new technique to perform an evolutionary lateral tuning of the linguistic variables with a simple technique for rule selection (that removes unnecessary rules). To do so, the tuning technique considers a new rule representation scheme by using the linguistic 2-tuples representation model which allows the lateral variation of the involved linguistic labels.
Supported by the Spanish Ministry of Science and Technology under Project TIC-2002-04036-C05-01.
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References
Alcalá, R., Benítez, J.M., Casillas, J., Cordón, O., Pérez, R.: Fuzzy control of HVAC systems optimized by genetic algorithms. Applied Intelligence 18, 155–177 (2003)
Alcalá, R., Herrera, F.: Genetic tuning on fuzzy systems based on the linguistic 2-tuples representation. In: Proc. of the IEEE Int. Conf. on Fuzzy Syst., vol. 1, pp. 233–238 (2004)
Alcalá, R., Casillas, J., Cordón, O., González, A., Herrera, F.: A genetic rule weighting and selection process for fuzzy control of HVAC systems. Engineering Applications of Artificial Intelligence 18(3), 279–296 (2005)
Calvino, F., Gennusa, M.L., Rizzo, G., Scaccianoce, G.: The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller. Energy and Buildings 36, 97–102 (2004)
Casillas, J., Cordón, O., del Jesus, M.J., Herrera, F.: Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction. IEEE T. Fuzzy Syst. 13(1), 13–29 (2005)
Eshelman, L.J., Schaffer, J.D.: Real-coded genetic algorithms and interval-schemata. Foundations of Genetic Algorithms 2, 187–202 (1993)
Gómez-Skarmeta, A.F., Jiménez, F.: Fuzzy modeling with hybrid systems. Fuzzy Sets Syst. 104, 199–208 (1999)
Herrera, F., Lozano, M., Verdegay, J.L.: Fuzzy connectives based crossover operators to model genetic algorithms population diversity. Fuzzy Sets Syst. 92(1), 21–30 (1997)
Herrera, F., Lozano, M., Verdegay, J.L.: A learning process for fuzzy control rules using genetic algorithms. Fuzzy Sets Syst. 100, 143–158 (1998)
Herrera, F., Martńez, L.: A 2-tuple fuzzy linguistic representation model for computing with words. IEEE T. Fuzzy Syst. 8(6), 746–752 (2000)
Huang, S., Nelson, R.M.: Rule development and adjustment strategies of a fuzzy logic controller for an HVAC system - Parts I and II (analysis and experiment). ASHRAE Trans. 100(1), 841–850, 851–856 (1994)
Ishibuchi, H., Murata, T., Türksen, I.B.: Single-objective and two objective genetic algorithms for selecting linguistic rules for pattern classification problems. Fuzzy Sets Syst. 89(2), 135–150 (1997)
Krone, A., Krause, H., Slawinski, T.: A new rule reduction method for finding interpretable and small rule bases in high dimensional search spaces. In: Proc. of the IEEE Int. Conf. on Fuzzy Syst., vol. 2, pp. 693–699 (2000)
Krone, A., Taeger, H.: Data-based fuzzy rule test for fuzzy modelling. Fuzzy Sets Syst. 123(3), 343–358 (2001)
Whitley, D., Kauth, J.: GENITOR: A different genetic algorithm. In: Proc. of the Rocky Mountain Conf. on Artificial Intelligence, pp. 118–130 (1988)
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Alcalá, R., Alcalá-Fdez, J., Berlanga, F.J., Gacto, M.J., Herrera, F. (2006). Improving Fuzzy Rule-Based Decision Models by Means of a Genetic 2-Tuples Based Tuning and the Rule Selection. In: Torra, V., Narukawa, Y., Valls, A., Domingo-Ferrer, J. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2006. Lecture Notes in Computer Science(), vol 3885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11681960_31
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DOI: https://doi.org/10.1007/11681960_31
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