000 | 03556nam a22005175i 4500 | ||
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001 | 978-3-319-52881-6 | ||
003 | DE-He213 | ||
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007 | cr nn 008mamaa | ||
008 | 170202s2017 sz | s |||| 0|eng d | ||
020 |
_a9783319528816 _9978-3-319-52881-6 |
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024 | 7 |
_a10.1007/978-3-319-52881-6 _2doi |
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_aUYQ _2bicssc |
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_aUYQ _2thema |
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_a006.3 _223 |
100 | 1 |
_aCpałka, Krzysztof. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _958683 |
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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. |
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300 |
_aXI, 196 p. 65 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aStudies in Computational Intelligence, _x1860-9503 ; _v684 |
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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 |
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650 | 0 |
_aArtificial intelligence. _93407 |
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650 | 1 | 4 |
_aComputational Intelligence. _97716 |
650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
710 | 2 |
_aSpringerLink (Online service) _958684 |
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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 |