Abstract
Fuzzy controller generating procedures when using crisp input-output data produce the necessary system in two steps: first they produce a starting rule set and then they tune the parameters that influence the approximation with a learning algorithm. Other solutions work under special conditions as hybrid neuro-fuzzy systems improving the approximation with a gradient based learning algorithm (e.g. in the case of monotonous membership functions), or use the methods of the genetic algorithms to generate the fuzzy controller. This article demonstrates a new method which reduces the problem to a classification task and carries out the generation of the rules and the tuning of the system in a single step.
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References
Borgulya, I.: A Ranking Method for Multiple Criteria Decision Making. International Journal of Systems Science 28, (1997) 905–912
Borgulya, I.: Két fuzzy osztályozó módszer. Szigma XXIX. (1998) No. 1–2. 7–28
Chin T.C., Qi X.M.: Genetic algorithms for learning the rule base of fuzzy logic controller. Fuzzy Sets and Systems 97. (1998) 1–7
Cho K.B., Wang B.H.: Radial basis function based adaptive fuzzy systems and their applications to system identification and predection. Fuzzy Sets and Systems 83, (1996) 325–339
Cordón O., Herrera F.: A Hybrid Genetic Algorithm-Evolution Strategy Process for Learning Fuzzy Logic Controller Knowledge Bases In: F. Herrera, L.J. Verdegay (eds): Genetic Algorithms and Soft Computing. Physica-Verlag, Heidelberg (1996) 250–278
Fullér R.: Egy fuzzy-neurális megközelítés a keresztárfolyamoktól függő portfóliók kiértékelésére. XXIII. Magyar Operációkutatási Konferencia Pécs. (1997)
Geyer-Schulz A.: Fuzzy genetic programming and dynamic decision making Proc. ICSE’96 (1996) 155–172
Halgamuge S.K., Glesner M.: Neural networks in designed fuzzy systems for real world applications. Fuzzy Sets and Systems 65, (1994) 1–12
Höppner F., Klawonn F., Kruse R.: Fuzzy-Clusteranalyse. Vieweg, Braunschweig /Wiesbaden (1997)
Hornik K., Stinchgombe M.: Multilayer Feedforward Networks Are Universal Approximators. In: White H.: Artificial Neural Network. Approximation and Learning Theory. Blackwell Pub. Cambridge (1992) 12–28
Kosko B.: Neural Networks and Fuzzy Systems. Englewood Cliffs, NJ. Prentice Hall (1992)
E.S. Lee, Q. Zhu: Fuzzy and evidence Reasoning. Physica-Verlag Heidelberg (1995)
Mitra S., Pal S.K.: Fuzzy multi-layer perceptron inferencing and rule generation. IEEE Trans. Neural Networks 6, (1995) 51–63
Munda G.: Multicriteria Evaluation in a Fuzzy Environment. Physica-Verlag Heidelberg (1995)
Shimojima K., Kubota N., Fukuda T.: Virus-Evolutionary Algorithm for Fuzzy Controller Optimization. In: F. Herrera, L.J. Verdegay (eds): Genetic Algorithms and Soft Computing. Physica Verlag Heidelberg (1996) 367–388
Sugeno M., Yakusawa T.: A fuzzy logic-based approach to qualitative modeling. IEEE Trans. On Fuzzy Systems, Vol. 1. (1993)
Wang L.X., Mendel J.M.: “Back-propagation fuzzy system as nonlinear dynamic system Identifiers”. Proc. First IEEE International Conference on Fuzzy Systems, (1992) 1409–1416
Wang L.X.: Universal approximation by hierarchical fuzzy systems. Fuzzy Sets and Systems. 93, (1998) 223–230
Zhang J., Knoll A., Le K.V.: A New Type of Fuzzy Logic System for Adaptive Modelling and Control. In: Reusch (ed): Computation Intelligence. Springer Berlin (1997) 363–380
Yager R.R.: Fuzzy Decision Making Including Unequal Objectives. Fuzzy Sets and Systems. 1, (1978) 87–95
Yager R.R., Filev D.: Generation of Fuzzy Rules by Mountain Clustering. Journal of Intelligent & Fuzzy systems, Vol. 2, (1994) No. 3, 209–219
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© 1999 Springer-Verlag Berlin Heidelberg
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Borgulya, I. (1999). Fuzzy Controller Generation with a Fuzzy Classification Method. In: Reusch, B. (eds) Computational Intelligence. Fuzzy Days 1999. Lecture Notes in Computer Science, vol 1625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48774-3_9
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DOI: https://doi.org/10.1007/3-540-48774-3_9
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