Andere Kunden interessierten sich auch für
![Machine Learning for Earth Sciences]( "Machine Learning for Earth Sciences")
Machine Learning for Earth Sciences44,99 €![Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard]( "Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard")
Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard106,99 €![Sustainable IoT and Data Analytics Enabled Machine Learning Techniques and Applications]( "Sustainable IoT and Data Analytics Enabled Machine Learning Techniques and Applications")
Sustainable IoT and Data Analytics Enabled Machine Learning Techniques and Applications117,99 €![Machine Learning for Earth Sciences]( "Machine Learning for Earth Sciences")
Machine Learning for Earth Sciences59,99 €![Machine learning classifiers &Classifier ensample]( "Machine learning classifiers &Classifier ensample")
Machine learning classifiers &Classifier ensample29,99 €![Earthquake Prediction by Seismic Electric Signals]( "Earthquake Prediction by Seismic Electric Signals")
Earthquake Prediction by Seismic Electric Signals125,99 €![Feature Papers for Land Innovations - Data and Machine Learning]( "Feature Papers for Land Innovations - Data and Machine Learning")
Feature Papers for Land Innovations - Data and Machine Learning95,99 €
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).