| 書(shū)目名稱 | Knowledge-Driven Board-Level Functional Fault Diagnosis | | 編輯 | Fangming Ye,Zhaobo Zhang,Xinli Gu | | 視頻video | http://file.papertrans.cn/545/544240/544240.mp4 | | 概述 | Explains and applies optimized techniques from the machine-learning domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing.Demonstrates techniques based | | 圖書(shū)封面 |  | | 描述 | This book provides a comprehensive set of characterization, prediction, optimization, evaluation, and evolution techniques for a diagnosis system for fault isolation in large electronic systems. Readers with a background in electronics design or system engineering can use this book as a reference to derive insightful knowledge from data analysis and use this knowledge as guidance for designing reasoning-based diagnosis systems. Moreover, readers with a background in statistics or data analytics can use this book as a practical case study for adapting data mining and machine learning techniques to electronic system design and diagnosis. This book identifies the key challenges in reasoning-based, board-level diagnosis system design and presents the solutions and corresponding results that have emerged from leading-edge research in this domain. It covers topics ranging from highly accurate fault isolation, adaptive fault isolation, diagnosis-system robustness assessment, to system performance analysis and evaluation, knowledge discovery and knowledge transfer. With its emphasis on the above topics, the book provides an in-depth and broad view of reasoning-based fault diagnosis system | | 出版日期 | Book 2017 | | 關(guān)鍵詞 | Functional Fault Diagnosis; Intelligent Fault Diagnosis; Data-Driven Design of Fault Diagnosis; Resilie | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-40210-9 | | isbn_softcover | 978-3-319-82054-5 | | isbn_ebook | 978-3-319-40210-9 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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