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020 _a9789819704910
_9978-981-97-0491-0
024 7 _a10.1007/978-981-97-0491-0
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aLi, Fangyi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9100394
245 1 0 _aFuzzy Rule-Based Inference
_h[electronic resource] :
_bAdvances and Applications in Reasoning with Approximate Knowledge Interpolation /
_cby Fangyi Li, Qiang Shen.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXIV, 187 p. 44 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _a1 Introduction -- 2 Framework of Fuzzy Rule Interpolation -- 3 Attribute Weighting and Weighted Fuzzy Rule Bases -- 4 Attribute Weighted Fuzzy Rule-based Inference -- 5 Attribute Weighted Fuzzy Interpolative Reasoning -- 6 Practical Integrated Weighted Approximate Reasoning -- 7 Practical Application to Interpretable Medical Risk Analysis -- 8 Conclusion.
520 _aThis book covers a comprehensive approach to the development and application of a suite of novel algorithms for practical approximate knowledge-based inference. It includes an introduction to the fundamental concepts of fuzzy sets, fuzzy logic, and fuzzy inference. Collectively, this book provides a systematic tutorial and self-contained reference to recent advances in the field of fuzzy rule-based inference. Approximate reasoning systems facilitate inference by utilizing fuzzy if-then production rules for decision-making under circumstances where knowledge is imprecisely characterized. Compositional rule of inference (CRI) and fuzzy rule interpolation (FRI) are two typical techniques used to implement such systems. The question of when to apply these potentially powerful reasoning techniques via automated computation procedures is often addressed by checking whether certain rules can match given observations. Both techniques have been widely investigated to enhance the performance of approximate reasoning. Increasingly more attention has been paid to the development of systems where rule antecedent attributes are associated with measures of their relative significance or weights. However, they are mostly implemented in isolation within their respective areas, making it difficult to achieve accurate reasoning when both techniques are required simultaneously. This book first addresses the issue of assigning equal significance to all antecedent attributes in the rules when deriving the consequents. It presents a suite of weighted algorithms for both CRI and FRI fuzzy inference mechanisms. This includes an innovative reverse engineering process that can derive attribute weightings from given rules, increasing the automation level of the resulting systems. An integrated fuzzy reasoning approach is then developed from these two sets of weighted improvements, showcasing more effective and efficient techniques for approximate reasoning. Additionally, the book provides an overarching application to interpretable medical risk analysis, thanks to the semantics-rich fuzzy rules with attribute values represented in linguistic terms. Moreover, it illustrates successful solutions to benchmark problems in the relevant literature, demonstrating the practicality of the systematic approach to weighted approximate reasoning.
650 0 _aArtificial intelligence.
_93407
650 0 _aExpert systems (Computer science).
_93392
650 0 _aComputers, Special purpose.
_946653
650 0 _aPattern recognition systems.
_93953
650 0 _aApplication software.
_9100397
650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_9100399
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aKnowledge Based Systems.
_979172
650 2 4 _aSpecial Purpose and Application-Based Systems.
_946654
650 2 4 _aAutomated Pattern Recognition.
_931568
650 2 4 _aComputer and Information Systems Applications.
_9100401
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
700 1 _aShen, Qiang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_926332
710 2 _aSpringerLink (Online service)
_9100402
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819704903
776 0 8 _iPrinted edition:
_z9789819704927
776 0 8 _iPrinted edition:
_z9789819704934
856 4 0 _uhttps://doi.org/10.1007/978-981-97-0491-0
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cEBK
999 _c87816
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