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Backward fuzzy rule interpolation

Jin, Shangzhu.

Backward fuzzy rule interpolation

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Backward fuzzy rule interpolationby Shangzhu Jin, Qiang Shen, Jun Peng.
作者:	Jin, Shangzhu.
其他作者:	Shen, Qiang.
出版者:	Singapore :Springer Singapore :2019.
面頁冊數:	xvii, 159 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Fuzzy sets.
電子資源:	https://doi.org/10.1007/978-981-13-1654-8
ISBN:	9789811316548$q(electronic bk.)

Backward fuzzy rule interpolation
Jin, Shangzhu.

Backward fuzzy rule interpolation[electronic resource] /by Shangzhu Jin, Qiang Shen, Jun Peng. - Singapore :Springer Singapore :2019. - xvii, 159 p. :ill., digital ;24 cm.

Introduction -- Background: Fuzzy Rule Interpolation (FRI) -- BFRI with a Single Missing Antecedent Value (S-BFRI) -- BFRI with Multiple Missing Antecedent Values (M-BFRI) -- An Alternative BFRI Method -- Backward rough-fuzzy rule interpolation -- Application: Terrorism Risk Assessment using BFRI -- Conclusion -- Appendix A Publications Arising from the Thesis -- Appendix B List of Acronyms -- Appendix C Glossary of terms -- Bibliography.

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 is extended 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 studies based upon a real-world scenario of terrorism risk assessment are provided in order to demonstrate the potential and efficacy of the hierarchical fuzzy rule interpolation methodology.

ISBN: 9789811316548$q(electronic bk.)

Standard No.: 10.1007/978-981-13-1654-8doiSubjects--Topical Terms:

182529
Fuzzy sets.

LC Class. No.: QA248.5 / .J55 2019

Dewey Class. No.: 511.3223

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505 0 $a Introduction -- Background: Fuzzy Rule Interpolation (FRI) -- BFRI with a Single Missing Antecedent Value (S-BFRI) -- BFRI with Multiple Missing Antecedent Values (M-BFRI) -- An Alternative BFRI Method -- Backward rough-fuzzy rule interpolation -- Application: Terrorism Risk Assessment using BFRI -- Conclusion -- Appendix A Publications Arising from the Thesis -- Appendix B List of Acronyms -- Appendix C Glossary of terms -- Bibliography.
520 $a 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 is extended 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 studies based upon a real-world scenario of terrorism risk assessment are provided in order to demonstrate the potential and efficacy of the hierarchical fuzzy rule interpolation methodology.
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