What is it about?
The method of singular perturbations is an original form of principal component analysis, inspired by control science. This work shows the use of this method to reduce Markov chain models, in order to make them more operational for a first pre-analysis phase.
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Why is it important?
Most of Markov models of real systems are huge and difficult to analyze. A pertinent pre-analysis algorithm keeping the essential part of it is fundamental.
Perspectives
All the set of PCA methods are inspired by these approaches. The application to stochastic models has a tremendous importance and offers an interesting, discriminative interpretation of the notion of slow and fast dynamics - at the origin of the singular perturbation method.
Professor Daniel Racoceanu
Pontifical Catholic University of Peru
Read the Original
This page is a summary of: Use of Singular Perturbations for the Reduction of Manufacturing System Models, IFAC Proceedings Volumes, July 2000, Elsevier,
DOI: 10.1016/s1474-6670(17)39373-4.
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