Function Approximation Using Probabilistic Fuzzy Systems
AbstractWe consider function approximation by fuzzy systems. Fuzzy systems are typically used for approximating deterministic functions, in which the stochastic uncertainty is ignored. We propose probabilistic fuzzy systems in which the probabilistic nature of uncertainty is taken into account. Furthermore, these systems take also fuzzy uncertainty into account by their fuzzy partitioning of input and output spaces. We discuss an additive reasoning scheme for probabilistic fuzzy systems that leads to the estimation of conditional probability densities, and prove how such fuzzy systems compute the expected value of this conditional density function. We show that some of the most commonly used fuzzy systems can compute the same expected output value and we derive how their parameters should be selected in order to achieve this goal.
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Bibliographic InfoPaper provided by Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam. in its series Research Paper with number ERS-2011-026-LIS.
Date of creation: 13 Dec 2011
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fuzzy set; additive reasoning; function approximation; fuzzy partitioning; probabilistic fuzzy system;
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- Berg, J. van den & Bergh, W.M. van den & Kaymak, U., 2001. "Probabilistic and Statistical Fuzzy Set Foundations of Competitive Exception Learning," Research Paper ERS-2001-40-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
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