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Anbieter: Books Puddle, New York, NY, USA
Zustand: New. pp. 252.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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In den WarenkorbPaperback. Zustand: Brand New. 246 pages. 9.20x6.10x0.60 inches. In Stock.
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Taschenbuch. Zustand: Neu. Condition Monitoring Using Computational Intelligence Methods | Applications in Mechanical and Electrical Systems | Tshilidzi Marwala | Taschenbuch | xvi | Englisch | 2014 | Springer | EAN 9781447161349 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Condition Monitoring Using Computational Intelligence Methods promotes the various approaches gathered under the umbrella of computational intelligence to show how condition monitoring can be used to avoid equipment failures and lengthen its useful life, minimize downtime and reduce maintenance costs. The text introduces various signal-processing and pre-processing techniques, wavelets and principal component analysis, for example, together with their uses in condition monitoring and details the development of effective feature extraction techniques classified into frequency-, time-frequency- and time-domain analysis. Data generated by these techniques can then be used for condition classification employing tools such as:- fuzzy systems; rough and neuro-rough sets; neural and Bayesian networks;hidden Markov and Gaussian mixture models; and support vector machines.
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In den WarenkorbPaperback. Zustand: Like New. Like New. book.
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In den WarenkorbZustand: new. Questo è un articolo print on demand.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Condition Monitoring Using Computational Intelligence Methods promotes the various approaches gathered under the umbrella of computational intelligence to show how condition monitoring can be used to avoid equipment failures and lengthen its useful life, minimize downtime and reduce maintenance costs. The text introduces various signal-processing and pre-processing techniques, wavelets and principal component analysis, for example, together with their uses in condition monitoring and details the development of effective feature extraction techniques classified into frequency-, time-frequency- and time-domain analysis. Data generated by these techniques can then be used for condition classification employing tools such as:- fuzzy systems; rough and neuro-rough sets; neural and Bayesian networks;hidden Markov and Gaussian mixture models; and support vector machines. 252 pp. Englisch.
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In den WarenkorbKartoniert / Broschiert. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Helps the practitioner prevent machine failure, improving safety and cutting costs Shows the reader how computational intelligence can provide an efficacious alternative to traditional visual inspection techniques Uses on-line learning meth.
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EUR 142,62
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In den WarenkorbZustand: New. Print on Demand pp. 252.
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
Zustand: New. PRINT ON DEMAND pp. 252.
Sprache: Englisch
Verlag: Springer, Springer Feb 2014, 2014
ISBN 10: 1447161343 ISBN 13: 9781447161349
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Condition Monitoring Using Computational Intelligence Methods promotes the various approaches gathered under the umbrella of computational intelligence to show how condition monitoring can be used to avoid equipment failures and lengthen its useful life, minimize downtime and reduce maintenance costs. The text introduces various signal-processing and pre-processing techniques, wavelets and principal component analysis, for example, together with their uses in condition monitoring and details the development of effective feature extraction techniques classified into frequency-, time-frequency- and time-domain analysis. Data generated by these techniques can then be used for condition classification employing tools such as:¿ fuzzy systems; rough and neuro-rough sets; neural and Bayesian networks;hidden Markov and Gaussian mixture models; and support vector machines.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 252 pp. Englisch.