國立高雄大學圖資館 |

Language: English

Back

Backward fuzzy rule interpolation

Jin, Shangzhu.

Backward fuzzy rule interpolation

Record Type:	Electronic resources : Monograph/item
Title/Author:	Backward fuzzy rule interpolationby Shangzhu Jin, Qiang Shen, Jun Peng.
Author:	Jin, Shangzhu.
other author:	Shen, Qiang.
Published:	Singapore :Springer Singapore :2019.
Description:	xvii, 159 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Fuzzy sets.
Online resource:	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

Backward fuzzy rule interpolation
LDR:02813nmm a2200325 a 4500 001 562923
003 DE-He213
005 20191023141706.0
006 m d
007 cr nn 008maaau
008 200227s2019 si s 0 eng d
020 $a 9789811316548$q(electronic bk.)
020 $a 9789811316531$q(paper)
024 7 $a 10.1007/978-981-13-1654-8 $2 doi
035 $a 978-981-13-1654-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA248.5 $b .J55 2019
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 511.3223 $2 23
090 $a QA248.5 $b .J61 2019
100 1 $a Jin, Shangzhu. $3 848252
245 1 0 $a Backward fuzzy rule interpolation $h [electronic resource] / $c by Shangzhu Jin, Qiang Shen, Jun Peng.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2019.
300 $a xvii, 159 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Fuzzy sets. $3 182529
650 0 $a Fuzzy mathematics. $3 243725
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Simulation and Modeling. $3 273719
650 2 4 $a Computer-Aided Engineering (CAD, CAE) and Design. $3 274500
700 1 $a Shen, Qiang. $3 378510
700 1 $a Peng, Jun. $3 848253
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-981-13-1654-8
950 $a Intelligent Technologies and Robotics (Springer-42732)