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

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發(fā)表于 2025-3-21 18:47:50 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)Backward Fuzzy Rule Interpolation
影響因子2023Shangzhu Jin,Qiang Shen,Jun Peng
視頻videohttp://file.papertrans.cn/181/180230/180230.mp4
發(fā)行地址Focuses on a novel approach: backward fuzzy rule interpolation and extrapolation (BFRI), which could significantlyexpand the applications of fuzzy rule interpolation and fuzzy inference.Proposes two t
圖書(shū)封面Titlebook: Backward Fuzzy Rule Interpolation; Shangzhu Jin,Qiang Shen,Jun Peng Book 2019 Springer Nature Singapore Pte Ltd. 2019 Artificial Intellige
影響因子.This book chiefly presents a novel approach referred to as backward fuzzy rule interpolation and extrapolation (BFRI). BFRI allows observations that directly relate to the conclusion to be inferred or interpolated from other antecedents and conclusions. Based on the scale and move transformation interpolation, this approach supports both interpolation and extrapolation, which involve multiple hierarchical intertwined fuzzy rules, each with multiple antecedents. As such, it offers a means of broadening the applications of fuzzy rule interpolation and fuzzy inference. The book deals with the general situation, in which there may be more than one antecedent value missing for a given problem. Two techniques, termed the .parametric approach .and .feedback approach., are proposed in an attempt to perform backward interpolation with multiple missing antecedent values. In addition, to further enhance the versatility and potential of BFRI, the backward fuzzy interpolation method isextended to support α-cut based interpolation by employing a fuzzy interpolation mechanism for multi-dimensional input spaces (IMUL). Finally, from an integrated application analysis perspective, experimental stu
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https://doi.org/10.1007/978-3-540-31211-6ing indirect interpolative reasoning which involves several intertwined fuzzy rules, each with multiple antecedents. However, no existing technique, (including BFRI) considers the case where multiple antecedents are absent.
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Transformation Based Backward Fuzzy Rule Interpolation with Multiple Missing Antecedent Values,ing indirect interpolative reasoning which involves several intertwined fuzzy rules, each with multiple antecedents. However, no existing technique, (including BFRI) considers the case where multiple antecedents are absent.
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發(fā)表于 2025-3-22 17:48:53 | 只看該作者
Application: Terrorism Risk Assessment Using BFRI, Terrorism risk assessment (TRA) therefore plays a crucial role in national and international security. In order to predict terrorist behaviour from a given set of evidence (including hypothesised scenarios), it is often necessary for investigators to reconstruct the possible scenarios that may have taken place.
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Transformation Based Backward Fuzzy Rule Interpolation with Multiple Missing Antecedent Values,issing 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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發(fā)表于 2025-3-23 05:43:56 | 只看該作者
Application: Terrorism Risk Assessment Using BFRI,e to have a comprehensive security risk management programme including effective risk assessment and appropriate decision support for such activities. Terrorism risk assessment (TRA) therefore plays a crucial role in national and international security. In order to predict terrorist behaviour from a
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