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001			95362
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006			m o d
007			cr cnu|||unuuu
008			170210s2017 sz a ob 001 0 eng d
020			_a3319528807
020			_a3319528815 _q(electronic bk.)
020			_a9783319528809
020			_a9783319528816 _q(electronic bk.)
020			_z9783319528809 _q(print)
035			_a(OCoLC)972093310 _z(OCoLC)981814080 _z(OCoLC)1005833057 _z(OCoLC)1011853458
040			_aGW5XE _cGW5XE _dYDX _dOCLCF _dUAB _dCOO _dIOG _dAZU _dUWO _dVT2 _dUPM _dESU _dZ5A _dJBG _dIAD _dICW _dICN _dOTZ _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aQA402 _b.C635 2017 EB
100	1		_aCpałka, Krzysztof, _eautor
245	1	0	_aDesign of interpretable fuzzy systems _cKrzysztof Cpałka.
264		1	_aCham, Switzerland _bSpringer _c2017
300			_a1 recurso en línea (xi, 196 páginas) _bilustraciones
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aStudies in computational intelligence _x1860-949X _vvolume 684
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas e índice
505	0		_aPreface -- Acknowledgements -- Chapter1: Introduction -- Chapter2: Selected topics in fuzzy systems designing -- Chapter3: Introduction to fuzzy system interpretability -- Chapter4: Improving fuzzy systems interpretability by appropriate selection of their structure -- Chapter5: Interpretability of fuzzy systems designed in the process of gradient learning -- Chapter6: Interpretability of fuzzy systems designed in the process of evolutionary learning -- Chapter7: Case study: interpretability of fuzzy systems applied to nonlinear modelling and control -- Chapter8: Case study: interpretability of fuzzy systems applied to identity verification -- Chapter9: Concluding remarks and future perspectives -- Index.
520	3		_aThis book shows that the term "interpretability" goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics.
650		7	_aLógica difusa _2embne _0(OCoLC)fst00936807 _0 _9152594
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-52881-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017B
998			_b02/2018 _dz _e- _zSI
999			_c95362 _d95362 _x1