Prediction of Terrorist Attacks in China based on BP improved Algorithm and GTD

DOI

10.2991/icmeit-19.2019.65How to use a DOI?

Keywords

BP network; GTD sample; number of terrorist attacks; prediction.

Abstract

Due to the different national conditions, the driving forces and factors of national terrorist attacks vary. Therefore, this paper takes GTD China sample data as the research object to study and predict. The prediction process is as follows: On the basis of the BP network-based model for predicting the most dangerous areas, combined with the GTD sample data, the best number of nodes in the implicit layer of the prediction model is automatically selected by combining the empirical formula with the MATLAB program. Three improved BP algorithms are used to train the network model. The results show that the training error of Levenburg Marquardt algorithm is minimal and the convergence speed is fastest. Through the training and simulation of the model, it is proved that the model has high precision and can meet the requirement of practical application.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Le Hong
PY  - 2019/04
DA  - 2019/04
TI  - Prediction of Terrorist Attacks in China based on BP improved Algorithm and GTD
BT  - Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
PB  - Atlantis Press
SP  - 403
EP  - 407
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.65
DO  - 10.2991/icmeit-19.2019.65
ID  - Hong2019/04
ER  -