A Particle Swarm Optimization-Backpropagation (PSO-BP) Model for the Prediction of Earthquake in Japan-Reference-Cited by-同舟云学术

A Particle Swarm Optimization-Backpropagation (PSO-BP) Model for the Prediction of Earthquake in Japan

Published:2019 Issue: Volume: Page:435-441
ISSN:2194-5357
Container-title:Emerging Research in Computing, Information, Communication and Applications
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Author:

Abraham Abey,Rohini V.

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-13-5953-8_36

Reference10 articles.

1. Pannakkat, A., & Adeli, H. (2007). Neural network models for earthquake magnitude prediction using multiple seismicity indicators. International Journal of Neural Systems, 17(1), 13–33.

2. Arjun, C. R., & Kumar, A. (2009, March). Artificial neural network—Based estimation of peak ground acceleration. ISET Journal of Earthquake Technology, Paper no. 501, 46(1), 19–28.

3. Alarifi A. S., & Alarifi, N. S. (2009). Earthquake magnitude prediction using artificial neural network in northern red sea area. American Geophysical union, fall meeting 2009.

4. Adeli, H., & Panakkat, A. A probabilistic neural network for earthquake magnitude prediction. Neural Networks, 22 (in press).

5. Widrow, B., & Lehr, M. A. (1990). 30 years of adaptive neural networks: Perceptron, madaline, and backpropagation. Proceedings of the IEEE, 78(9), 1415–1442.

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