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- W2979955537 abstract "To make the fuzzy discrete event system theory more useful in a wide range of applications, it is crucial to equip it with learning capability, an issue that has hardly been touched upon in the literature. In this paper, we develop stochastic-gradient-decent-based algorithms for online learning of the event transition matrices and parameters of the fuzzy sets fuzzifying input variables of a fuzzy discrete event system modeled as a fuzzy automaton. We uncover an intrinsic obstacle in the algorithms that significantly impedes the learning owing to dependences of the fuzzy set parameters on the matrix. As a remedy, we modify the algorithms using the exponential penalty function. Preliminary computer simulation results are presented to illustrate learning performance of our final algorithms." @default.
- W2979955537 created "2019-10-18" @default.
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- W2979955537 date "2019-06-01" @default.
- W2979955537 modified "2023-10-02" @default.
- W2979955537 title "Fuzzy Discrete Event Systems with Gradient-Based Online Learning" @default.
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- W2979955537 doi "https://doi.org/10.1109/fuzz-ieee.2019.8859012" @default.
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