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010   $a981-13-1654-6
024 7 $a10.1007/978-981-13-1654-8
035   $a(CKB)4100000005679059
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035   $a(DE-He213)978-981-13-1654-8
035   $a(EXLCZ)994100000005679059
100   $a20180812d2019      u|             0
101 0 $aeng
135   $aurcnu||||||||
181   $2rdacontent
182   $2rdamedia
183   $2rdacarrier
200 10$aBackward Fuzzy Rule Interpolation$b[electronic resource] /$fby Shangzhu Jin, Qiang Shen, Jun Peng
210  1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2019.
215   $a1 online resource (167 pages) $cillustrations
311   $a981-13-1653-8 
320   $aIncludes bibliographical references.
327   $aIntroduction -- 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.
330   $aThis 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 ?-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.
606   $aEngineering
606   $aArtificial intelligence
606   $aComputer simulation
606   $aComputer aided design
606   $aComputational Intelligence$3http://scigraph.springernature.com/things/product-market-codes/T11014
606   $aArtificial Intelligence$3http://scigraph.springernature.com/things/product-market-codes/I21000
606   $aSimulation and Modeling$3http://scigraph.springernature.com/things/product-market-codes/I19000
606   $aComputer-Aided Engineering (CAD, CAE) and Design$3http://scigraph.springernature.com/things/product-market-codes/I23044
615  0$aEngineering.
615  0$aArtificial intelligence.
615  0$aComputer simulation.
615  0$aComputer aided design.
615 14$aComputational Intelligence.
615 24$aArtificial Intelligence.
615 24$aSimulation and Modeling.
615 24$aComputer-Aided Engineering (CAD, CAE) and Design.
676   $a006.3
700   $aJin$b Shangzhu$4aut$4http://id.loc.gov/vocabulary/relators/aut$01228745
702   $aShen$b Qiang$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aPeng$b Jun$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910484779003321
996   $aBackward Fuzzy Rule Interpolation$92852654
997   $aUNINA