05110nam a22006255i 4500 978-981-97-0491-0 DE-He213 20251030111809.0 cr nn 008mamaa 240408s2024 si | s |||| 0|eng d 9789819704910 978-981-97-0491-0 10.1007/978-981-97-0491-0 doi Q334-342 TA347.A78 UYQ bicssc COM004000 bisacsh UYQ thema Li, Fangyi. author. aut http://id.loc.gov/vocabulary/relators/aut 74857 Fuzzy Rule-Based Inference [electronic resource] : Advances and Applications in Reasoning with Approximate Knowledge Interpolation / by Fangyi Li, Qiang Shen. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XIV, 187 p. 44 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 1 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. This 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. Artificial intelligence. 58441 Expert systems (Computer science). 67822 Computers, Special purpose. Pattern recognition systems. 65547 Application software. Image processing Digital techniques. 64228 Computer vision. Artificial Intelligence. 58441 Knowledge Based Systems. 67823 Special Purpose and Application-Based Systems. Automated Pattern Recognition. 65551 Computer and Information Systems Applications. Computer Imaging, Vision, Pattern Recognition and Graphics. 65550 Shen, Qiang. author. aut http://id.loc.gov/vocabulary/relators/aut 74858 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819704903 Printed edition: 9789819704927 Printed edition: 9789819704934 https://doi.org/10.1007/978-981-97-0491-0 ZDB-2-SCS ZDB-2-SXCS 59006 59006