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020			_a9789811316548
024	7		_a10.1007/978-981-13-1654-8 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQA248.5 _b2019 EB
100	1		_aJin, Shangzhu _eautor _9670909
245	1	0	_aBackward fuzzy rule interpolation _cby Shangzhu Jin, Qiang Shen, Jun Peng
264		1	_aSingapore _bSpringer Singapore : _bImprint: Springer _c2019
300			_a1 recurso en línea (XVII, 159 páginas) _b44 ilustraciones, 27 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_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.
520	3		_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 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.
988			_aPrimersemestre_2019_Robotics
650		7	_2embne _aConjuntos difusos _9145903
650		7	_2embne _aConjuntos, Teoría de _9405124
650		7	_2embne _9140864 _aÁlgebra
700	1		_aShen, Qiang _eautor _9670910
700	1		_aPeng, Jun _eautor _9670911
776	0	8	_iPrinted edition: _z9789811316531
776	0	8	_iPrinted edition: _z9789811316555
776	0	8	_iPrinted edition: _z9789811346613
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-13-1654-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b10/2019 _eel _zSI