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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (Advances in Computer Vision and Pattern Recognition) by Aldrich, Chris, Auret, Lidia is a nonfiction available to read on EtoBox.

What is Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (Advances in Computer Vision and Pattern Recognition) about?

Algorithms for intelligent fault diagnosis of automated operations offer significant benefits to the manufacturing and process industries. Furthermore, machine learning methods enable such monitoring systems to handle nonlinearities and large volumes of data. 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: Reviews the application of machine learning to process monitoring and fault diagnosis 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 dyn

Who reads Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (Advances in Computer Vision and Pattern Recognition)?

Author

Aldrich, Chris, Auret, Lidia

Publisher

Springer London : Imprint : Springer

Published

2013

Language

EN

ISBN

9781447151845

Category

nonfiction

Subjects

Computer Science, Science, Engineering