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The fuzzy inference system with model based on association rules
Języki publikacji
Abstrakty
In the practice of company management we deal with many tasks, that are associated with limited knowledge and uncertainty about the course of events and activities managed objects. Fuzzy IF-THEN rules are an appropriate form of describing subjective uncertainty results from lack of knowledge and objective uncertainty results from characteristics of different processes. On the other hand, the uncertainty due to randomness can be described using the theory of probability. The paper presents inference system with probabilistic-fuzzy knowledge base as a tool which can help user to analyze complete uncertainty of real problems in the company using fuzzy sets and probability. In the mentioned system, knowledge is saved in the weighted IF-THEN fuzzy rules, where the weights constitute marginal probabilities of the fuzzy events in the antecedents and conditional probabilities of the fuzzy events in the consequents. Moreover, this paper propose using fuzzy association rules as a method of automatic knowledge base extraction in the inference system. For this purpose a modification of the Apriori algorithm was described. The algorithm extracts the most important and matching linguistic rules by assumption of minimum support as a minimum joint probability of the events in the rules. If minimum support equals zero, then the rules present total probabilistic distribution of the fuzzy events, otherwise the rules present probabilistic distribution, which is the best matching to a variables universe. In the methodology of system creation, the universe of quantitative variables is discretized on disjoint intervals of variable values and the fuzzy sets are defined by grades of membership of the disjoint intervals to fuzzy sets. This approach allows vectorize the calculation. A numerical example is analyzed by using a wind speed prediction process. Parameter of wind speed characterized by high variability of random character. However, the correct estimation of wind speed, as a energy resources, is necessary for control working of wind turbine. It is also important for the localization process of wind turbines, production planning and estimating cost-effectiveness of such investments.
Czasopismo
Rocznik
Tom
Strony
50--60
Opis fizyczny
Bibliogr. 26 poz.
Twórcy
autor
autor
- Katedra Automatyki i Systemów Informatycznych, Instytut Automatyki i Informatyki, Wydział Elektrotechniki, Automatyki i Informatyki, Politechnika Opolska, ul. K. Sosnowskiego 31, 45-272 Opole, a.walaszek-babiszewska@po.opole.pl
Bibliografia
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- [2] Alcalá-Fdez J., Alcalá R., Gacto M. J., Herrera F.: Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms. Fuzzy Sets and System, Vol. 160, 2009, pp. 905.921.
- [3] Błaszczyk K.: Implementation of a probabilistic-fuzzy modeling system in Matlab. X Międzynarodowe Warsztaty Doktoranckie, OWD, Warszawa 2008, str. 74-78.
- [4] Błaszczyk K.: Notes on Defining fuzzy sets in the created inference system with probabilistic-fuzzy knowledge base. IV Środowiskowe Warsztaty Doktorantów PO, Zeszyty Naukowe Politechniki Opolskiej Z. 63, Elektryka, Nr 335/2010, Opole . Pokrzywna 2010, str. 9-10.
- [5] Chen G., Wei Q.: Fuzzy Association Rules and the Extended Mining Algorithm. Information Sciences. Vol. 147, 2002, pp. 201-228.
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- [17] Nowicki R. K.: Rozmyte systemy decyzyjne w zadaniach z ograniczoną wiedzą. Akademicka Oficyna Wydawnicza EXIT, Warszawa 2009.
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- [21] Walaszek-Babiszewska A.: Zbiory rozmyte jako narzędzie formalizacji wiedzy ekspertów w systemach informatycznych. Zeszyty Naukowe Politechniki Śląskiej, Organizacja i Zarządzanie. Materiały Krajowej Konferencji Naukowej Wiedza . Informacja . Marketing, Szczyrk 2004.
- [22] Walaszek-Babiszewska A., Chudzicki M.: Fuzzy model for the information and decision making support system for the CFM branch company. Applied Computer Science, Vol. 2, No. 1, 2006, Decision Support Engineering, Banaszak Z., Matuszek J. (Eds.), pp. 110-121.
- [23] Walaszek-Babiszewska A.: Fuzzy Knowledge Representation Using Probability Measures of Fuzzy Events, [in:] Automation and Robotics, Juan Manuel Ramos Arreguin (Ed.), pp. 329-342, I-Tech Education and Publishing, Vienna 2008.
- [24] Walaszek-Babiszewska A., Błaszczyk K.: A modified Apriori algorithm to generate rules for inference system with probabilistic-fuzzy knowledge base. 7th Workshop on Advanced Control and Diagnosis 19-20 November 2009, Zielona Góra, CD-ROOM.
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Bibliografia
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bwmeta1.element.baztech-article-LOD9-0021-0007