Items related to Machine Learning models for seismic signals parameters
- ISBN 10 620202626X
- ISBN 13 9786202026260
- BindingPaperback
- LanguageEnglish
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Machine Learning models for seismic signals parameters
Published by
LAP LAMBERT Academic Publishing Sep 2017, 2017
ISBN 10: 620202626X
ISBN 13: 9786202026260
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this work, advanced machine learning algorithms are used to develop predictive models for forecasting ground motion parameters. The machine learning algorithms used are extreme learning machines (ELM), support vector regression (SVR) and its three variations, decision trees and hybrid algorithm ANFIS (adaptive neuro fuzzy inference system). A novel neuro fuzzy algorithm, RANFIS (randomized ANFIS) is also proposed for forecasting ground motion parameters. This advanced learning machine integrates the explicit knowledge of the fuzzy systems with the learning capabilities of neural networks, as in the case of conventional adaptive neuro fuzzy inference system (ANFIS). In RANFIS, to accelerate the learning speed without compromising the generalization capability, the fuzzy layer parameters are not tuned. The ground motion parameters predicted are peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The model is developed using real earthquake records obtained from the database released by PEER (Pacific Earthquake Engineering Research Center). 200 pp. Englisch. Seller Inventory # 9786202026260
Quantity: 2 available
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Machine Learning models for seismic signals parameters
Published by
LAP LAMBERT Academic Publishing
ISBN 10: 620202626X
ISBN 13: 9786202026260
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this work, advanced machine learning algorithms are used to develop predictive models for forecasting ground motion parameters. The machine learning algorithms used are extreme learning machines (ELM), support vector regression (SVR) and its three variations, decision trees and hybrid algorithm ANFIS (adaptive neuro fuzzy inference system). A novel neuro fuzzy algorithm, RANFIS (randomized ANFIS) is also proposed for forecasting ground motion parameters. This advanced learning machine integrates the explicit knowledge of the fuzzy systems with the learning capabilities of neural networks, as in the case of conventional adaptive neuro fuzzy inference system (ANFIS). In RANFIS, to accelerate the learning speed without compromising the generalization capability, the fuzzy layer parameters are not tuned. The ground motion parameters predicted are peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The model is developed using real earthquake records obtained from the database released by PEER (Pacific Earthquake Engineering Research Center). Seller Inventory # 9786202026260
Quantity: 1 available
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Machine Learning models for seismic signals parameters
Published by
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 620202626X
ISBN 13: 9786202026260
New
Softcover
Seller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Thomas SoniaI am Doctorate in Computer Science and Engineering from Indian Institue of Technology Roorkee, India. My area of interest is algorithms, M L, pattern recognition and its application to real world complex problems. I have . Seller Inventory # 174172051
Quantity: Over 20 available