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Fuzzy Pattern Recognition Based on Symmetric Fuzzy Relative Entropy

机译:基于对称模糊相对熵的模糊模式识别

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摘要

Based on fuzzy similarity degree, entropy, relative entropy and fuzzy entropy, the symmetric fuzzy relative entropy is presented, which not only has a full physical meaning, but also has succinct practicability. The symmetric fuzzy relative entropy can be used to measure the divergence between different fuzzy patterns. The example demonstrates that the symmetric fuzzy relative entropy is valid and reliable for fuzzy pattern recognition and classification, and its classification precision is very high.
机译:基于模糊相似度,熵,相对熵和模糊熵,提出了对称的模糊相对熵,它不仅具有完整的物理意义,而且具有较好的实用性。对称模糊相对熵可用于测量不同模糊模式之间的差异。实例表明,对称模糊相对熵对于模糊模式识别和分类是有效和可靠的,其分类精度很高。

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