Lins isis didier (9 Ergebnisse)
Support Vector Machines and Particle Swarm Optimization
Mrcio Das Chagas Moura Enrique Lpez Droguett Isis Didier Lins
Sprache: Englisch
Verlag: VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
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Support Vector Machines and Particle Swarm Optimization
Isis Didier Lins|Márcio das Chagas Moura|Enrique López Droguett
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Taschenbuch. Zustand: Neu. Support Vector Machines and Particle Swarm Optimization | Applications to Reliability Prediction | Isis Didier Lins (u. a.) | Taschenbuch | 92 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838319407 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 4…9078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Reliability is a critical indicator of organizations' performance in face of market competition, since it contributes to production regularity. Its prediction is of great interest as it may anticipate trends of system failures and thu…s enable maintenance actions. The consideration of all aspects that influence system reliability may render its modeling very complex and learning methods such as Support Vector Machines (SVMs) emerge as alternative prediction tools: previous knowledge about the function or process that maps input variables into output is not required. However, SVM performance is affected by parameters from the related learning problem. Suitable values for them are chosen by means of Particle Swarm Optimization (PSO), a probabilistic approach based on the behavior of organisms that move in groups. Thus, a PSO+SVM methodology is proposed to handle reliability prediction problems. It is used to solve application examples based on time series data and also involving data collected from oil production wells. The results indicate that PSO+SVM is able to provide competitive or even more accurate reliability predictions when compared, for example, to Neural Networks (NNs). 92 pp. Englisch.
Support Vector Machines and Particle Swarm Optimization
Das Chagas Moura Mrcio Lpez Droguett Enrique Lins Isis Didier
Sprache: Englisch
Verlag: VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
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Zustand: New. Print on Demand pp. 92 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.
Support Vector Machines and Particle Swarm Optimization
Das Chagas Moura Mrcio Lpez Droguett Enrique Lins Isis Didier
Sprache: Englisch
Verlag: VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
- Softcover
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Zustand: New. PRINT ON DEMAND pp. 92.
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Reliability is a critical indicator of organizations'' performance in face of market competition, since it contributes to production regularity. Its prediction is of great interest as it may anticipate trends of system failures and thus e…nable maintenance actions. The consideration of all aspects that influence system reliability may render its modeling very complex and learning methods such as Support Vector Machines (SVMs) emerge as alternative prediction tools: previous knowledge about the function or process that maps input variables into output is not required. However, SVM performance is affected by parameters from the related learning problem. Suitable values for them are chosen by means of Particle Swarm Optimization (PSO), a probabilistic approach based on the behavior of organisms that move in groups. Thus, a PSO+SVM methodology is proposed to handle reliability prediction problems. It is used to solve application examples based on time series data and also involving data collected from oil production wells. The results indicate that PSO+SVM is able to provide competitive or even more accurate reliability predictions when compared, for example, to Neural Networks (NNs).VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 92 pp. Englisch.
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Reliability is a critical indicator of organizations' performance in face of market competition, since it contributes to production regularity. Its prediction is of great interest as it may anticipate trends of system failures and thus ena…ble maintenance actions. The consideration of all aspects that influence system reliability may render its modeling very complex and learning methods such as Support Vector Machines (SVMs) emerge as alternative prediction tools: previous knowledge about the function or process that maps input variables into output is not required. However, SVM performance is affected by parameters from the related learning problem. Suitable values for them are chosen by means of Particle Swarm Optimization (PSO), a probabilistic approach based on the behavior of organisms that move in groups. Thus, a PSO+SVM methodology is proposed to handle reliability prediction problems. It is used to solve application examples based on time series data and also involving data collected from oil production wells. The results indicate that PSO+SVM is able to provide competitive or even more accurate reliability predictions when compared, for example, to Neural Networks (NNs).
