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020 _a9783319528816
_9978-3-319-52881-6
024 7 _a10.1007/978-3-319-52881-6
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aCpałka, Krzysztof.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_958683
245 1 0 _aDesign of Interpretable Fuzzy Systems
_h[electronic resource] /
_cby Krzysztof Cpałka.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXI, 196 p. 65 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v684
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 _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 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
710 2 _aSpringerLink (Online service)
_958684
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319528809
776 0 8 _iPrinted edition:
_z9783319528823
776 0 8 _iPrinted edition:
_z9783319850061
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v684
_958685
856 4 0 _uhttps://doi.org/10.1007/978-3-319-52881-6
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80195
_d80195