Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods is popular PDF and ePub book, written by Chris Aldrich in 2013-06-15, it is a fantastic choice for those who relish reading online the Computers genre. Let's immerse ourselves in this engaging Computers book by exploring the summary and details provided below. Remember, Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods can be Read Online from any device for your convenience.

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods Book PDF Summary

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.

Detail Book of Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods PDF

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
  • Author : Chris Aldrich
  • Release : 15 June 2013
  • Publisher : Springer Science & Business Media
  • ISBN : 9781447151852
  • Genre : Computers
  • Total Page : 388 pages
  • Language : English
  • PDF File Size : 20,6 Mb

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