Fuzzy inference neural networks with fuzzy parameters

• Autorzy: Rutkowska D. , Hayashi Y.
• Języki publikacji: EN

Abstrakty

EN

This paper concerns fuzzy neural networks and fuzzy inference neural networks, which are two different approaches to neuro-fuzzy combinations. The former is a direct fuzzification of artificial neural networks by introducing fuzzy signals and fuzzy weights. The latter is a representation of fuzzy systems in the form of multi-layer connectionist networks, similar to neural networks. Parameters of membership functions (centers and widths) play the role of neural network weights. In this paper, fuzzy inference neural networks with fuzzy parameters are considered. Neuro-fuzzy systems of this kind utilize both approaches: fuzzy neural networks and fuzzy inference neural networks. They also pertain to fuzzy systems of type 2 since membership functions with fuzzy parameters characterize type 2 fuzzy sets. Various architectures of these networks have been obtained for fuzzy systems based on different fuzzy implications. By analogy with fuzzy inference neural networks with crisp parameters, methods of learning fuzzy parameters and rule generation can be derived for neuro-fuzzy systems with fuzzy parameters. Fuzzy inference neural networks are studied in the framework of fuzzy granulation. In particular, fuzzy clustering as fuzzy information granulation is proposed to be applied in order to generate fuzzy IF-THEN rules. Applications of fuzzy inference neural networks are also outlined.

Słowa kluczowe

EN

neuro-fuzzy system; fuzzy neural networks; fuzzy inference neural networks; fuzzy systems of type 2; fuzzy granulation

• Wydawca: Politechnika Gdańska
• Czasopismo: TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk
• Rocznik: 2003
• Tom: Vol. 7, No 1
• Strony: 7--22
• Opis fizyczny: Bibliogr. 53 poz., rys.

Twórcy

autor

Rutkowska D.

• Department of Computer Engineering, Technical University of Czestochowa, Armii Krajowej 36, 42-200 Czestochowa, Poland

autor

Hayashi Y.

• Department of Computer Science, Meiji University, 1-1-1 Higashimita, Tama-ku, Kawasaki, 214-8571, Japan

Bibliografia

• [1] Buckley J J and Feuring T 1999 Fuzzy and Neural: Interactions and Applications, Springer- Verlag, Heidelberg, New York
• [2] Takagi H 2000 Int. J. Appl. Math. Comp. Sci. 10 (4) 647
• [3] Buckley J J and Hayashi Y 1992 Fuzzy Sets and Artificial Intelligence 1 11
• [4] Buckley J J and Hayashi Y 1994 Fuzzy Sets and Systems 66 1
• [5] Buckley J J and Havashi Y 1994 Fuzzy Sets, Neural Networks and Soft Computing (Zadeh L A and Yager R R, Eds.), Van Nostrand Reinhold, New York, pp. 233-249
• [6] Hayashi Y, Buckley J J and Czogała E 1993 Int. J. Intelligent Systems 8 527
• [7] Rutkowska D and Hayashi Y 1999 J. Adv. Comput. Intelligence 3 (3) 177
• [8] Zurada J M 1992 Introduction to Artificial Neural Systems, West Publishing Company
• [9] Wang L-X 1994 Adaptive Fuzzy Systems and Control, PTR Prentice Hall, Englewood Cliffs, New Jersey
• [10] Rutkowska D 2000 Fuzzy Control Theory and Practice (Hampel R, Wagenknecht M and Chaker N, Eds.), Springer-Verlag, Heidelberg, New York, pp. 277-286
• [11] Rutkowska D 2002 Neuro-Fuzzy Architectures and Hybrid Learning, Springer-Verlag, Heidelberg, New York
• [12] Rutkowska D and Nowicki R 2000 Int. J. Appl. Math. Comp. Sci. 10 (4) 675
• [13] Zadeh L A 1971 Aspects of Network and System Theory (Kalman R E and DeClaris N, Eds.), Holt, Rinehart and Winston, New York, pp. 209-245
• [14] Dubois D and Prade H 1980 Fuzzy Sets and Systems: Theory and Applications, Academic Press, London
• [15] Pedrycz W 1993 Fuzzy Control and Fuzzy Systems, Wiley, New York
• [16] Zadch L A 1975 Information Science Part I 8 199; Part II 8 301; Part III 9 43
• [17] Zadch L A 1965 Information and Control 8 (3) 338
• [18] Klement E P, Mesiar R and Pap E 2000 Triangular Norms, Kluwer, Dordrecht, Boston, London
• [19] Curry H 1944 Quart. Appl. Math. 2 258
• [20] Pilniski M 1996 Proc. 2nd Conf. Neural Networks and Their Applications, Szczyrk, Poland, pp. 383-391
• [21] Piliński M 1997 FLiNN - User Mannual, Polish Neural Network Society, Czestochowa, Poland (in Polish)
• [22] Rutkowska D, Piliński M and Rutkowski L 1997 Neural Networks. Genetic Algorithms, and Fuzzy Systems, PWN, Warsaw, Poland (in Polish)
• [23] Mizumoto M and Tanaka K 1976 Information and Control 31 312
• [24] Mizumoto M and Tanaka K 1981 Fuzzy Sets and Systems 5 277
• [25] John R 1998 Proc. 7th IEEE Int. Conf. Fuzzy Systems (FUZZ-IEEE’98), Anchorage, AK, USA, pp. 1003-1008
• [26] Hisdal E 1981 Int. J. Man-Machine Studies 15 385
• [27] Gorzałczany M 1989 Fuzzy Sets and Systems 31 243
• [28] Turksen I B 1995 Advances in Fuzzy Theory and Technology (Wang P, Ed.), Duke Univ., Durhan, NC, USA, pp. 31-81
• [29] Karnik N N and Mendel J M 1998 University of Southern California Report, http://sipi.use.cdu/~mendel/report
• [30] Turksen I B 1999 Fuzzy Sets and Systems 106 11
• [31] Karnik N N, Mendel J M and Liang Q 1999 IEEE Trans. Fuzzy Syst. 7 (6) 643
• [32] Liang Q and Mendel J M 2000 IEEE Trans. Fuzzy Syst. 8 (5) 535
• [33] Rutkowska D 1997 Intelligent Computational Systems. Genetic Algorithms and Neutral Networks in Fuzzy Systems, PLJ Academic Publishing House, Warsaw, Poland (in Polish)
• [34] Nowicki R and Rutkowska D 2001 Proc. East West Fuzzy Colloguium 2001, Zittau, Germany, pp. 207-213.
• [35] Wang L X and Mendel J M 1992IEEE Trans. Syst., Man, Cybern. 22 (6) 1414
• [36] Ishibushi H, Nozaki K, Yamamoto N and Tanaka H 1995 IEEE Trans. Fuzzy Syst. 3 260
• [37] Mitra S and Hayashi Y 2000 IEEE Trans. Neural Networks 11 (3) 7 18
• [38] John R, Innocent P R and Barnes M R 1997 Proc. 3rd Joint Conf. Information Science 1 58
• [39] Bezdek J C 1981 Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York
• [40] Rutkowska D and Starczewski A 1999 Proc. 4th Conf. Neural Networks and Their Applications, Zakopane, Poland, pp. 220-225
• [41] Rutkowska D and Starczewski A 2000 Fuzzy Systems in Medicine (Szczepaniak P S, Lisboa P J G and Kacprzyk J, Eds.), Springer-Verlag, Heidelberg, New York, pp. 503 -518
• [42] 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
• [43] Zadeh L A 1997 Fuzzy Sets and Systems 90 111
• [44] Zadeh L A 1996 IEEE Trans. Fuzzy Syst. 4 103
• [45] Zadeh L A 1999 IEEE Trans. Circ. Syst. – I: Fundamental Theory and Application 45 (1) 105
• [46] Zadeh L A 1996 Multiple Valued Logic 1 1
• [47] Mendel j l 1999 Proc 3rd Int. ICSC Symposium on Fuzzy Logic and Application, Rochester Univ., Rochester, NY, USA, pp. 158-164
• [48] Pedrycz W 2000 Int. J. Appl. Math. Comp. Sci 10 (4) 723
• [49] Hayashi Y 1992 Proc. 1st IEEE Int. Conf. Fuzzy Systems (Fuzz-IEEE’92), San Diego, CA, USA, pp. 485-491
• [50] Nauck D, Klawonn F and Kruso R 1997 Foundations of Neuro-Fuzzy Systems, Wiley, New York
• [51] Rutkowska D 2002 Proc. IEEE Int Conf. Fuzzy Systems (FUZZ-IEEE’02), The 2002 IEEE World Congress on Computational Intelligence, Honolulu, Hawaii, pp. 1180-1185
• [52] Czogała E and Łęski J 2000 Fuzzy and Neuro-Fuzzy Intelligent Systems, Springer-Verlag, Heidelberg, New York
• [53] Rutkowska D, Nowicki R and Hayashi Y 2002 Lecture Notes in Computer Science 2328 599