Zusammenfassung
Im Zusammenhang mit der Weiterentwicklung von Fuzzy-Systemen der zweiten Generation wird die Thematik der automatischen Generierung, Optimierung und Adaption solcher Systeme zunehmend an Bedeutung gewinnen. Einen Schwerpunkt bilden dabei in der Literatur die Untersuchungen bezüglich der Kombination von Fuzzy-Logik und neuronalen Netzen [TAK90, ICH91, JAN92, WM92, GR94]. Durch den aus der Fusion dieser Systeme resultierenden Synergieeffekt will man die erwünschten Eigenschaften beider Paradigmen vereinen und ihre Nachteile kompensieren.
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Literatur
G.E.P.Box, G.M.Jenkins: Time Series Analysis: Forecasting and Control, San Francisco CA: Holden Day; 1976
H. R.Berenji, P.Khedkar: Learning and Tuning Fuzzy Logic Controllers Through Reinforcements, IEEE Trans. on Neural Netw., Vol. 3, No. 5, Sept. 1992, pp. 724–740
D.Driankov, H.Hellendoorn, M.Redsterank: An Introduction to Fuzzy Control, Springer-Verlag Berlin Heidelberg, 1993
M. M.Gupta, D. H.Rao: On the Principles of Fuzzy Neural Networks, Fuzzy Sets and Systems, Vol. 61, No. 1, Jan. 1994, pp. 1–18
S.Horikawa, T.Furuhashi, S.Okuma, Y.Uchikawa: A Fuzzy Controller Using a Neural Network and its Capability to Learn Expert’s Control Rules, Proc. Intl Conf. on Fuzzy Logic & Neural Netw., IZUKA’90, Japan, 1990, pp. 103–106
H.Ichihashi: Iterative Fuzzy Modeling and a Hierarchical Network, Univ. of Osaka Prefecture, IFSA’91, Brüssel, Band E, 1991, pp. 155–158
J. R.Jang: Self-Learning Fuzzy Controllers Based on Temporal Back Propagation, IEEE Trans. on Neural Netw., Vol. 3, No. 5, Sept. 1992, pp. 714–723
W.McCulloch, W.Pitts: A Logical Calculus of the Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics, Vol. 5, 1943, pp. 115–133
W.Pedrycz: An Identification Algorithm in Fuzzy Relational Systems, Fuzzy Sets and Systems, Vol. 13, 1984, pp. 1–25
H.Surmann, A.Kanstein, K.Goser: Self-Organizing and Genetic Algorithms for an Automatic Design of Fuzzy Control and Decision Systems, Proc. of the EUFIT’93, Vol.1, Sept. 1993, pp. 1097–1104
M.Sugeno, T.Yasukawa: Linguistic Modeling Based on Numerical Data, Proc. of IFSA’91, Brussels: Computer, Management & Systems Science, 1991
H.Takagi: Fusion Technology of Fuzzy Theory and Neural Networks — Survey and Future Directions, First Int’l Conf. on Fuzzy Logic & Neural Netw., IZUKA’90, Japan, 1990, pp. 13–26
R.M.Tong: The Evaluation of Fuzzy Models Derived from Experimental Data, Fuzzy Sets and Systems, Vol.4, 1980, pp. 1–12
L.Wang, J.Mendel: Fuzzy Basis Functions, Universal Approximation, and Orthogonal Least-Squares Learning, IEEE Trans. on Neural Netw., Vol. 3, No. 5, Sept. 1992, pp. 807–814
C.-W.Xu, Y.-Z.LU: Fuzzy Model Identification and Self-Learning for Dynamic Systems, IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-17, 1987, pp.683–689
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© 1994 Springer-Verlag Berlin Heidelberg
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Hauptmann, W., Heesche, K. (1994). Ein Prototyp für ein integriertes Fuzzy-Neuro System. In: Reusch, B. (eds) Fuzzy Logik. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79386-8_48
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DOI: https://doi.org/10.1007/978-3-642-79386-8_48
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