| 書目名稱 | Introduction to Neuro-Fuzzy Systems |
| 編輯 | Robert Fullér |
| 視頻video | http://file.papertrans.cn/474/473955/473955.mp4 |
| 概述 | Contains numerous exercises with solutions.Starts from the basics of fuzzy sets and neural nets then provides a broad overview of integrated approaches |
| 叢書名稱 | Advances in Intelligent and Soft Computing |
| 圖書封面 |  |
| 描述 | Fuzzy sets were introduced by Zadeh (1965) as a means of representing and manipulating data that was not precise, but rather fuzzy. Fuzzy logic pro- vides an inference morphology that enables approximate human reasoning capabilities to be applied to knowledge-based systems. The theory of fuzzy logic provides a mathematical strength to capture the uncertainties associ- ated with human cognitive processes, such as thinking and reasoning. The conventional approaches to knowledge representation lack the means for rep- resentating the meaning of fuzzy concepts. As a consequence, the approaches based on first order logic and classical probablity theory do not provide an appropriate conceptual framework for dealing with the representation of com- monsense knowledge, since such knowledge is by its nature both lexically imprecise and noncategorical. The developement of fuzzy logic was motivated in large measure by the need for a conceptual framework which can address the issue of uncertainty and lexical imprecision. Some of the essential characteristics of fuzzy logic relate to the following [242]. ? In fuzzy logic, exact reasoning is viewed as a limiting case of ap- proximate reasoning. ? |
| 出版日期 | Textbook 2000 |
| 關鍵詞 | fuzzy; fuzzy logic; fuzzy set; fuzzy system; learning; linear optimization; logic; modeling; neuro-fuzzy sys |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-7908-1852-9 |
| isbn_softcover | 978-3-7908-1256-5 |
| isbn_ebook | 978-3-7908-1852-9Series ISSN 1867-5662 Series E-ISSN 1867-5670 |
| issn_series | 1867-5662 |
| copyright | Springer-Verlag Berlin Heidelberg 2000 |
|Archiver|手機版|小黑屋|
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