Fuzzy Rule-Based Inference: Advances and Applications in Reasoning with Approximate Knowledge Interpolation
暫譯: 模糊規則推理：近似知識插值的進展與應用

Li, Fangyi, Shen, Qiang

Fuzzy Rule-Based Inference: Advances and Applications in Reasoning with Approximate Knowledge Interpolation
  • 出版商: Springer
  • 出版日期: 2025-04-10
  • 售價: $5,860
  • 貴賓價: 9.5 折 $5,567
  • 語言: 英文
  • 頁數: 187
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9819704936
  • ISBN-13: 9789819704934

    • 海外代購書籍(需單獨結帳)

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商品描述

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.

商品描述(中文翻譯)

這本書涵蓋了一套新穎演算法的開發與應用，提供了一個全面的方法來進行實用的近似知識推理。書中包括了模糊集合、模糊邏輯和模糊推理的基本概念介紹。整體而言，這本書提供了一個系統性的教程和自足的參考資料，針對模糊規則基推理領域的最新進展。

近似推理系統透過利用模糊的如果-則生產規則來促進推理，適用於知識不精確的情況下進行決策。組合推理規則（Compositional Rule of Inference, CRI）和模糊規則插值（Fuzzy Rule Interpolation, FRI）是實現這類系統的兩種典型技術。何時透過自動計算程序應用這些潛在強大的推理技術的問題，通常是透過檢查某些規則是否能夠匹配給定的觀察來解決。這兩種技術都已被廣泛研究，以提升近似推理的性能。越來越多的注意力被放在開發系統上，這些系統中規則的前提屬性與其相對重要性或權重的度量相關。然而，它們大多數是在各自的領域中孤立實施，這使得在同時需要這兩種技術時，實現準確推理變得困難。

本書首先解決了在推導結果時，對規則中所有前提屬性賦予相等重要性的問題。它提出了一套針對CR和FRI模糊推理機制的加權演算法。這包括一個創新的逆向工程過程，能夠從給定的規則中推導屬性權重，從而提高所產生系統的自動化水平。接著，從這兩組加權改進中開發出一種綜合的模糊推理方法，展示了更有效和高效的近似推理技術。此外，這本書還提供了一個針對可解釋的醫療風險分析的整體應用，得益於語義豐富的模糊規則，屬性值以語言術語表示。此外，它還展示了在相關文獻中基準問題的成功解決方案，證明了這種系統化方法在加權近似推理中的實用性。

作者簡介

Fangyi Li received the BSc and the PhD degrees in computer science and technology from Northwestern Polytechnical University, Xi'an, China, in 2014 and 2021, respectively. She also received the PhD degree in computational intelligence from Aberystwyth University, Aberystwyth, UK, in 2020. She is a lecturer with the School of Artificial Intelligence, Beijing Normal University, Beijing, China. Her current research interests include approximate reasoning, fuzzy rule interpolation, machine learning, and affective computing, with their practical applications.

Qiang Shen received a PhD in computing and electrical engineering (1990) from Heriot-Watt University, UK, and a DSc in computational intelligence (2013) from Aberystwyth University, UK. He holds the established chair of Computer Science and is pro vice-chancellor: faculty of business and physical sciences at Aberystwyth University. He is a fellow of the Royal Academy of Engineering and a fellow and council member of the Learned Society of Wales. The citation for his election to FREng stated that "Professor Shen is distinguished for world-leading and groundbreaking research and development of computational intelligence methodologies for data modelling and analysis, particularly for approximate knowledge-based critical intelligent decision support systems, with increased level of automation, efficiency and reliability. He is also a visionary academic leader, inspiring and nurturing future generations of computing engineers globally." He was a London 2012 Olympic Torch Relay torchbearer, selected to carry the Olympic torch in celebration of the centenary of Alan Turing. Professor Shen is the recipient of the 2024 IEEE Computational Intelligence Society Fuzzy Systems Pioneer Award.


作者簡介(中文翻譯)

方怡莉於2014年和2021年分別在中國西安的西北工業大學獲得計算機科學與技術的學士和博士學位。她於2020年在英國阿伯里斯特威斯大學獲得計算智能的博士學位。她目前是中國北京師範大學人工智慧學院的講師。她的研究興趣包括近似推理、模糊規則插值、機器學習和情感計算及其實際應用。

沈強於1990年在英國赫瑞瓦特大學獲得計算與電氣工程的博士學位，並於2013年在英國阿伯里斯特威斯大學獲得計算智能的科學博士學位。他擔任阿伯里斯特威斯大學計算機科學的教授及商業與物理科學學院的副校長。他是英國皇家工程院的院士，也是威爾士學術學會的院士及理事會成員。他當選為英國皇家工程院院士的評語指出：「沈教授因其在數據建模和分析的計算智能方法論方面的世界領先和開創性研究與發展而著稱，特別是在近似知識基礎的關鍵智能決策支持系統中，提升了自動化、效率和可靠性。他也是一位具有遠見的學術領袖，激勵並培養全球未來的計算工程師。」他曾是2012年倫敦奧運會火炬接力的火炬手，因慶祝艾倫·圖靈百年誕辰而被選中擔任奧運火炬的傳遞者。沈教授是2024年IEEE計算智能學會模糊系統先驅獎的獲得者。

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