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Abstrakty
A perception-based interpretation of evaluation systems is proposed in this paper. Thus, a perception-based approach to create intelligent systems is considered. The evaluation systems can be employed eg. in order to assess student exams, as well as to other applications. Evaluation marks used in these systems are given as perceptions expressed by words. The words play the role of labels of perceptions, and are represented by fuzzy sets. This means that the idea of perception-based systems, introduced by Zadeh, is applied. Various algorithms of overall assessment are suggested in this paper. Overall evaluation is produced as an aggregation of component evaluation marks. Systems of this kind can be obtained using fuzzy neurons, so fuzzy neural networks are also mentioned as a method of perception-based reasoning. The usefulness in artificial intelligence of both fuzzy sets and neural networks, and especially a combination of these, is shown.
Wydawca
Rocznik
Tom
Strony
131--145
Opis fizyczny
Bibliogr. 31 poz., rys., tab.
Twórcy
autor
- Department of Computer Engineering, Technical University of Czestochowa, Armii Krajowej 36, 42-200 Czestochowa, Poland
Bibliografia
- [1] Zadeh L A 1999 IEEE Trans. Circ. Syst. – I: Fundamental Theory and Application45(1) 105
- [2] Zadeh L A 2001 AI Magazine22(1) 73
- [3] Zadeh L A 1996 IEEE Trans. Fuzzy Syst.4103
- [4] Zadeh L A 1974 Information Processing74591
- [5] Zadeh L A 1965 Information and Control8(3) 338
- [6] Rutkowska D 2002 Neuro-Fuzzy Architectures and Hybrid Learning, Springer-Verlag, Heidel-berg, New York
- [7] Dubois D and Prade H 1980 Fuzzy Sets and Systems: Theory and Applications, AcademicPress, New York, London
- [8] Rutkowska D, Piliński M and Rutkowski L 1997 Neural Networks, Genetic Algorithms, and Fuzzy Systems, PWN, Warsaw, Poland (in Polish)
- [9] Klir G J 2000 Fuzzy Sets: An Overview of Fundamentals, Applications, and Personal Views,Beijing Normal Univ. Press, Beijing
- [10] Zimmermann H-J 1991 Fuzzy Set Theory and its Applications, (second edition) Kluwer,Boston, Dordrecht, London
- [11] Calvo T, Gaspar M and Mesiar R 2002 Aggregation Operators: New Trends and Applications,Springer-Verlag, Heidelberg, New York
- [12] Driankov D, Hellendoorn H and Reinfrank M 1993 An Introduction to Fuzzy Control,Springer-Verlag, Heidelberg, New York
- [13] Klir G J and Yuan B 1988 Fuzzy Sets, Uncertainty, and Information, Prentice Hall,Englewood Cliffs, New Jersey
- [14] Kaufmann A and Gupta M M 1985 Introduction to Fuzzy-Arithmetic: Theory and Applica-tions, Van Nostrand, New York
- [15] Pedrycz W 1993 Fuzzy Control and Fuzzy Systems, Wiley, New York
- [16] Zadeh L A 1971 Information Science3177
- [17] Moore R E 1966 Interval Analysis, Prentice Hall, Englewood Cliffs, NJ
- [18] Alefeld G and Herzberger J 1983 Introduction to Interval Computation, Academic Press, NewYork
- [19] Żurada J M 1992 Introduction to Artificial Neural Systems, West Publishing Company
- [20] Rutkowska D and Hayashi Y 1999 J. Adv. Comp. Intelligence3 (3) 177
- [21] Buckley J J and Feuring T 1999 Fuzzy and Neural: Interactions and Applications, Springer-Verlag, Heidelberg, New York
- [22] Lee S C and Lee E T 1970 Proc. 4thPrinceton Conf. Information Science Systems, pp. 381–385
- [23] McCulloch W S and Pitts W 1943 Bull. Math. Biophysics5115
- [24] Lee S C and Lee E T 1974 J. Cybernetics483
- [25] Grmela A 1997 Neural Computing and Neural Science: A Theory of Information Processingin the Human Brain, AGCES, Praha, Czech Republic
- [26] Zadeh L A 1979 Advances in Fuzzy Set Theory and Applications(Gupta M, Ragade R and Yager R, Eds.), North Holland, Amsterdam, pp. 3–18
- [27] Zadeh L A 1997 Fuzzy Sets and Systems90111
- [28] Zadeh L A 1975 Inform. Sci.8199;ibid.301
- [29] Zadeh L A 1975Inform. Sci.943
- [30] Rutkowska D and Hayashi Y 2002 TASK Quart.6(4) 1 (in this volume)
- [31] Pedrycz W 2000 Int. J. Appl. Math. Comp. Sci.10(4) 723
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT3-0009-0038
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