书目名称 | Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods |
编辑 | Chris Aldrich,Lidia Auret |
视频video | http://file.papertrans.cn/943/942529/942529.mp4 |
概述 | Describes the latest developments in nonlinear methods and their application in fault diagnosis.Discusses in detail several advances in machine learning theory.Contains numerous case studies with real |
丛书名称 | Advances in Computer Vision and Pattern Recognition |
图书封面 |  |
描述 | This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis. |
出版日期 | Book 2013 |
关键词 | Classification Trees; Fault Detection; Fault Identification; Kernel-based Methods; Neural Networks; Regre |
版次 | 1 |
doi | https://doi.org/10.1007/978-1-4471-5185-2 |
isbn_softcover | 978-1-4471-7160-7 |
isbn_ebook | 978-1-4471-5185-2Series ISSN 2191-6586 Series E-ISSN 2191-6594 |
issn_series | 2191-6586 |
copyright | Springer-Verlag London 2013 |