Titlebook: Backward Fuzzy Rule Interpolation; Shangzhu Jin,Qiang Shen,Jun Peng Book 2019 Springer Nature Singapore Pte Ltd. 2019 Artificial Intellige

不如樂死去

https://doi.org/10.1007/978-981-13-1654-8Artificial Intelligence; Approximation Reasoning; Fuzzy Logic; Fuzzy Interpolation; Backward Fuzzy Inter

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978-981-13-4661-3Springer Nature Singapore Pte Ltd. 2019

空氣

https://doi.org/10.1007/978-3-540-31211-6issing antecedent value to be interpolated in a backward fashion by exploiting the other given antecedents and the consequent. S-BFRI?works by performing indirect interpolative reasoning which involves several intertwined fuzzy rules, each with multiple antecedents. However, no existing technique, (

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混合

Carbon-Monoxide

Expediency

Physical Fractionation of Organic MatterApproximate reasoning (AR) Shen and Leitch in IEEE Trans Syst Man Cybern 23:1038–1061 (1993), [1], Synthese 30:407–408 (1975), [2]) is a group of methodologies and techniques, which concentrate on the processing of inexact information containing imprecision and uncertainty in artificial intelligence (AI) and computational intelligence (CI).

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https://doi.org/10.1007/978-3-540-31211-6Fuzzy rule interpolation significantly improves the robustness of fuzzy reasoning. It provides a way to reduce the complexity of fuzzy systems by omitting those rules which can be approximated by their neighbouring ones.

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