Fuzzy Rule-Based Inference: Advances and Applications in Reasoning with Approximate Knowledge Interpolation
Li, Fangyi, Shen, Qiang
- 出版商: Springer
- 出版日期: 2024-04-09
- 售價: $7,010
- 貴賓價: 9.5 折 $6,660
- 語言: 英文
- 頁數: 187
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 9819704901
- ISBN-13: 9789819704903
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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)
本書涵蓋了一種全面的方法，用於開發和應用一套新穎的算法，以進行實際的近似基於知識的推理。它包括對模糊集合、模糊邏輯和模糊推理的基本概念的介紹。總的來說，本書提供了一個系統化的教程和自包含的參考，介紹了模糊規則推理領域的最新進展。
近似推理系統通過利用模糊的if-then產生規則進行推理，用於在知識不精確的情況下進行決策。組合推理規則（CRI）和模糊規則插值（FRI）是兩種常用的實現這些系統的技術。通常通過檢查某些規則是否能夠匹配給定的觀察結果來解決何時應用這些潛在強大的推理技術的問題。這兩種技術已經得到廣泛研究，以提高近似推理的性能。越來越多的注意力被付予與規則前提屬性相關的相對重要性或權重的系統開發。然而，它們大多在各自的領域中獨立實施，當需要同時使用這兩種技術時，很難實現準確的推理。
本書首先解決了在推導結果時將所有前提屬性賦予相等重要性的問題。它提出了一套加權算法，用於CRI和FRI模糊推理機制。其中包括一個創新的逆向工程過程，可以從給定的規則中推導出屬性的權重，提高了結果系統的自動化水平。然後，從這兩組加權改進中開發了一種集成的模糊推理方法，展示了更有效和高效的近似推理技術。此外，由於具有語義豐富的模糊規則和以語言術語表示的屬性值，本書還提供了一個關於可解釋的醫療風險分析的整體應用。此外，它還展示了對相關文獻中的基準問題的成功解決方案，展示了加權近似推理系統的系統化方法的實用性。
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)
Fangyi Li於2014年和2021年分別獲得中國西安的西北工業大學計算機科學與技術學士學位和博士學位。她還於2020年獲得英國阿伯里斯特威斯大學計算智能博士學位。她現任北京師範大學人工智能學院講師。她目前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，以及它們的實際應用。
Qiang Shen於1990年獲得英國赫瑞瓦特大學計算和電氣工程博士學位，並於2013年獲得英國阿伯里斯特威斯大學計算智能博士學位。他擔任阿伯里斯特威斯大學計算機科學的正式主席，並擔任副校長：商業和物理科學學院。他是英國皇家工程學院的院士，也是威爾斯學會的院士和理事會成員。他當選為院士的引文稱：“Shen教授以世界領先和開創性的研究和開發計算智能方法論為特點，尤其是用於近似基於知識的關鍵智能決策支持系統的數據建模和分析，具有更高的自動化、效率和可靠性。他還是一位有遠見的學術領袖，激勵和培養全球未來一代的計算工程師。”他是倫敦2012年奧運火炬接力火炬手，被選為慶祝艾倫·圖靈百年的奧運火炬手。Shen教授是2024年IEEE計算智能學會模糊系統先驅獎的獲獎者。
