A. Bonarini, \Reinforcement distribution to fuzzy classi ers: a methodology to extend crisp algorithms," in IEEE International Conference on Evolutionary Computation { WCCI-ICEC'98, Piscataway, NJ, 1998, vol. 1, pp. 51-56, IEEE Computer Press. Home/Search Document Not in Database Summary Related Articles Check |
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An Approach to the Design of Reinforcement Functions.. - Bonarini, Bonacina.. Self-citation (Bonarini) (Correct)
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A. Bonarini, \Reinforcement distribution to fuzzy classi ers: a methodology to extend crisp algorithms," in IEEE International Conference on Evolutionary Computation { WCCI-ICEC'98, Piscataway, NJ, 1998, vol. 1, pp. 51-56, IEEE Computer Press.
Fuzzy and Crisp Representations - Of Real-Valued Input Self-citation (Bonarini) (Correct)
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A. Bonarini. Reinforcement distribution to fuzzy classi ers: a methodology to extend crisp algorithms. In IEEE International Conference on Evolutionary Computation { WCCI-ICEC'98, volume 1, pages 51-56, Piscataway, NJ, 1998. IEEE Computer Press.
Fuzzy and Crisp Representations of Real-valued Input.. - Bonarini, Bonacina.. (2000) (1 citation) Self-citation (Bonarini) (Correct)
....the con guration space (c space) We remind here that the c space is the space of the variables needed to completely describe the relevant aspects of a system, in general, and of a robot, in our case. In section 3, we propose to adopt fuzzy intervals as a representation of the classi er input [2] [3]. Considering an interval based model and a fuzzy one with the same number of intervals, the information content of a fuzzy representation is very close to that of a real valued representation, and considerably higher than that of an interval based. Moreover, the selection of certain well known ....
....sensors and actuators. The aim of this paper is to discuss the issues concerning learning crisp and fuzzy interval representations when operating with real valued input output; in particular we do not introduce new RL algorithms, presented and compared with other proposals elsewhere [6] 1] 2] [3], 4] nor we discuss about genetics, generalization, or the reinforcement function in our proposal. The considerations wepresentin this paper are general enough to be relevantforany RL algorithm operating on real values. 2 Grids, granularity, and real values The information needed by a LCS is ....
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A. Bonarini. Reinforcement distribution to fuzzy classiers: a methodology to extend crisp algorithms. In IEEE International Conference on Evolutionary Computation { WCCI-ICEC'98,volume 1, pages 51-56, Piscataway, NJ, 1998. IEEE Computer Press.
Learning Fuzzy Classifier Systems: Architecture and Exploration.. - Matteucci (2000) (Correct)
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A. Bonarini. Reinforcement distribution to fuzzy classi ers: a methodology to extend crisp algorithms. In IEEE International Conference on Evolutionary Computation { WCCI-ICEC'98, volume 1, pages 51-56, Piscataway, NJ, 1998. IEEE Computer Press.
A Learning Classifier Systems Bibliography - Kovacs, Lanzi (1999) (Correct)
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Andrea Bonarini. Reinforcement Distribution to Fuzzy Classiers. In Proceedings of the IEEE World Congress on Computational Intelligence (WCCI) { Evolutionary Computation, pages 51-56. IEEE Computer Press, 1998.
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