04043nam 22005655 450 991048477900332120181107132909.0981-13-1654-610.1007/978-981-13-1654-8(CKB)4100000005679059(MiAaPQ)EBC5495501(DE-He213)978-981-13-1654-8(EXLCZ)99410000000567905920180812d2019 u| 0engurcnu||||||||rdacontentrdamediardacarrierBackward Fuzzy Rule Interpolation[electronic resource] /by Shangzhu Jin, Qiang Shen, Jun PengSingapore :Springer Singapore :Imprint: Springer,2019.1 online resource (167 pages) illustrations981-13-1653-8 Includes bibliographical references.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.EngineeringArtificial intelligenceComputer simulationComputer aided designComputational Intelligencehttp://scigraph.springernature.com/things/product-market-codes/T11014Artificial Intelligencehttp://scigraph.springernature.com/things/product-market-codes/I21000Simulation and Modelinghttp://scigraph.springernature.com/things/product-market-codes/I19000Computer-Aided Engineering (CAD, CAE) and Designhttp://scigraph.springernature.com/things/product-market-codes/I23044Engineering.Artificial intelligence.Computer simulation.Computer aided design.Computational Intelligence.Artificial Intelligence.Simulation and Modeling.Computer-Aided Engineering (CAD, CAE) and Design.006.3Jin Shangzhuauthttp://id.loc.gov/vocabulary/relators/aut1228745Shen Qiangauthttp://id.loc.gov/vocabulary/relators/autPeng Junauthttp://id.loc.gov/vocabulary/relators/autBOOK9910484779003321Backward Fuzzy Rule Interpolation2852654UNINA