Fuzzy Rule-Based Inference (Record no. 87816)

000 -LEADER
fixed length control field 05120nam a22006255i 4500
001 - CONTROL NUMBER
control field 978-981-97-0491-0
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730171805.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240408s2024 si | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9789819704910
-- 978-981-97-0491-0
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
100 1# - AUTHOR NAME
Author Li, Fangyi.
245 10 - TITLE STATEMENT
Title Fuzzy Rule-Based Inference
Sub Title Advances and Applications in Reasoning with Approximate Knowledge Interpolation /
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2024.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XIV, 187 p. 44 illus.
505 0# - FORMATTED CONTENTS NOTE
Remark 2 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.
520 ## - SUMMARY, ETC.
Summary, etc 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.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
General subdivision Digital techniques.
700 1# - AUTHOR 2
Author 2 Shen, Qiang.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-981-97-0491-0
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Singapore :
-- Springer Nature Singapore :
-- Imprint: Springer,
-- 2024.
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-- text
-- txt
-- rdacontent
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-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Expert systems (Computer science).
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computers, Special purpose.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Pattern recognition systems.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Application software.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Image processing
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer vision.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Knowledge Based Systems.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Special Purpose and Application-Based Systems.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Automated Pattern Recognition.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer and Information Systems Applications.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer Imaging, Vision, Pattern Recognition and Graphics.
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-- ZDB-2-SCS
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-- ZDB-2-SXCS

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