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2018 | OriginalPaper | Chapter

Efficient Hidden Danger Prediction for Safety Supervision System: An Advanced Neural Network Learning Method

Authors : Zhigang Zhao, Yongfeng Wei, Xinyan Wang, Ruixin Li, Jing Deng

Published in: Proceedings of 2017 Chinese Intelligent Automation Conference

Publisher: Springer Singapore

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Abstract

Hidden danger prediction plays an important role in safety production and safety supervision. To improve the hidden danger prediction accuracy of tertiary industries in some small-medium cities, this paper utilizes extreme learning machine (ELM) algorithm to study the impact of relevant management index on the trend of hidden danger, and conduct hidden danger prediction. ELM is a novel learning algorithm for single hidden layer feedforward neural network (NN) with fast learning speed and good generalization performance. We use the nationwide enterprise hidden danger data to conduct the prediction experiment, and the comparisons between traditional NN learning method and ELM demonstrate the effectiveness and superiority of our method.

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Metadata
Title
Efficient Hidden Danger Prediction for Safety Supervision System: An Advanced Neural Network Learning Method
Authors
Zhigang Zhao
Yongfeng Wei
Xinyan Wang
Ruixin Li
Jing Deng
Copyright Year
2018
Publisher
Springer Singapore
Book
Proceedings of 2017 Chinese Intelligent Automation Conference

Print ISBN: 978-981-10-6444-9

Electronic ISBN: 978-981-10-6445-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-6445-6_51