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Backward Fuzzy Rule Interpolation

Af: Shangzhu Jin, Qiang Shen, Jun Peng Engelsk Paperback

Backward Fuzzy Rule Interpolation

Af: Shangzhu Jin, Qiang Shen, Jun Peng Engelsk Paperback
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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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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. 

Produktdetaljer
Sprog: Engelsk
Sider: 159
ISBN-13: 9789811346613
Indbinding: Paperback
Udgave:
ISBN-10: 9811346615
Udg. Dato: 2 feb 2019
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Verlag, Singapore
Oplagsdato: 2 feb 2019
Forfatter(e): Shangzhu Jin, Qiang Shen, Jun Peng
Forfatter(e) Shangzhu Jin, Qiang Shen, Jun Peng


Kategori Computer-aided design (CAD)


ISBN-13 9789811346613


Sprog Engelsk


Indbinding Paperback


Sider 159


Udgave


Længde 0mm


Bredde 155mm


Højde 235mm


Udg. Dato 2 feb 2019


Oplagsdato 2 feb 2019


Forlag Springer Verlag, Singapore

Kategori sammenhænge