Advantages of an approximate reasoning based on a fuzzy truth value

• Autorzy: Kudłacik P.
• Języki publikacji: EN

Abstrakty

EN

The approximate reasoning based on a fuzzy truth value is based on a different view of linguistic statements and comparing with the compositional rule of inference has some advantages. Benefits of the method are especially important for fuzzy expert systems with large sets of premises. The problem is very common for many applications in medicine, biology and biometry. By a short analysis of the approach and comparing to the compositional rule of inference the paper emphasizes the most important advantages of a possible implementation, which is particularly significant for the mentioned fields.

Słowa kluczowe

EN

fuzzy reasoning; fuzzy systems; fuzzy truth value

PL

wnioskowanie rozmyte; systemy rozmyte; wartości logiczne rozmyte

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2010
• Tom: Vol. 16
• Strony: 125--131
• Opis fizyczny: Bibliogr. 22 poz., rys.

Twórcy

autor

Kudłacik P.

• University of Silesia, Institute of Computer Science, 41-200 Sosnowiec, Będzińska 39, Poland

Bibliografia

• [1] BALDWIN J.F., A new approach to approximate reasoning using a fuzzy logic, Fuzzy Sets and Systems, Vol. 2, 1979, pp. 309–325.
• [2] BELLMAN R.E., ZADEH L.A., Modern Uses of Multiple-Valued Logic, chapter Local and Fuzzy Logics, 1977, pp. 103–165.
• [3] CHO Y.I., Fuzzy Inference Method for Intelligent Artificial System, Journal of Medical Informatics & Technologies, Vol. 13/2009, 2009, pp. 11-14.
• [4] CHRISTIAN R.S., SCHUH C., ADLASSING K.P., Medical Knowledge, Fuzzy Sets, and Expert Systems, Proceedings of Intelligent and Adaptive Systems in Medicine, Prague, March 31 - April 1, 2003.
• [5] FERNANDEZ J.C., VALLEJO E.E., MORETT E., Fuzzy C-means for inferring functional coupling of proteins from their phylogenetic profiles, Computational Intelligence and Bioinformatics and Computational Biology 2006, CIBCB '06, 2006 IEEE, Toronto 2006, pp. 1-8.
• [6] KUDŁACIK P., Operations on Fuzzy Sets with Piecewise Linear Membership Function, Studia Informatica, Vol. 29(3A), 2008 (In Polish), pp. 91–111.
• [7] KUDŁACIK P., Structure of Knowledge Base in The FUZZLIB Library, Studia Informatica, Vol. 31(2A), 2010 (In Polish), pp. 469–478.
• [8] ŁĘSKI J., Systemy Neuronowo-Rozmyte (Neuro-fuzzy Systems), WNT, Warsaw, 2008 (in Polish).
• [9] MA P.C.H., CHAN K.C.C., A Fuzzy Data Mining Technique for the Reconstruction of Gene Regulatory Networks from Time Series Expression Data, Computational Intelligence and Bioinformatics and Computational Biology 2006, CIBCB '06, 2006 IEEE, Toronto 2006, pp. 1-8.
• [10] MAMDANI E.H., ASSILAN S., An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies, Vol. 20(2), 1975, pp. 1–13.
• [11] PRZYBYŁA T., Hybrid Fuzzy Clustering Method. Journal of Medical Informatics & Technologies, Vol. 10/2006, 2009, pp. 143-150.
• [12] RESSOM H., NATARAJAN P., VARGHESE R.S., MUSAVI M.T., Applications of fuzzy logic in genomics, Fuzzy Sets and Systems, Vol. 152(1), 2005, pp. 125-138.
• [13] RUTKOWSKI L., Metody i Techniki Sztucznej Inteligencji (Methods and Techniques of Artificial Intelligence), PWN, Warsaw, 2006 (in Polish).
• [14] SARITAS I., OZKAN I.A., ALLAHVERDI N., ARGINDOGAN M., Determination of the drug dose by fuzzy expert system in treatment of chronic intestine inflammation, Journal of Intelligent Manufacturing, , Vol. 20(2), 2009, pp. 169-176.
• [15] SCHAEFER G., NAKASHIMA T., Data mining of gene expression data by fuzzy and hybrid fuzzy methods, IEEE Trans. Inf. Technol. Biomed, , Vol. 14(1) , 2009, pp. 23-9.
• [16] SUGENO M., KANG G.T., Structure identification of fuzzy model, Fuzzy sets and systems, Vol. 28, 1988, pp. 15-33.
• [17] TAKAGI H., SUGENO M., Fuzzy identification of systems and it’s application to modeling and control, IEEE Trens. Systems, Man and Cybernetics, Vol. 15(1) , 1985, pp. 16-132.
• [18] TSUKAMOTO Y., Advances in Fuzzy Set Theory and Applications, chapter: An Approach to Fuzzy Reasoning Method, North-Holland, Amsterdam, 1979, pp. 137–149.
• [19] ZADEH L.A., Outline of a new approach to the analysis of complex systems and decision processes, IEEE Transactions on Systems, Man and Cybernetics, Vol. 3(1) , 1973, pp. 28–44.
• [20] ZADEH L.A., The concept of a linguistic variable and it’s application to approximate reasoning, parts 1-3, Information Science, Vol. 8(8-9), 1975, pp. 199–249, 301–357, 43–80.
• [21] ZADEH L.A., Fuzzy logic and approximate reasoning. Syntheses, Vol. 30, 1975, pp. 407–428.
• [22] ZYGUŁA J., Fuzzy Logic Implementation for a Foot Pathology Recognition, Journal of Medical Informatics & Technologies, Vol. 1/2000, 2000, pp. 57-64.