Backward Fuzzy Rule Interpolation

Shangzhu Jin, Qiang Shen, Jun Peng

Research output: Book/ReportBook

1 Citation (SciVal)

Abstract

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.
Original languageEnglish
PublisherSpringer Nature
Number of pages159
ISBN (Electronic)9789811316548
ISBN (Print)9789811316531, 9789811346613
DOIs
Publication statusPublished - 06 Oct 2018

Keywords

  • Approximation reasoning
  • Artificial intelligence
  • Backward fuzzy interpoltion
  • Fuzzy interpolation
  • Fuzzy logic

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