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Machine Learning Based Key Performance Index Prediction Methods in Internet of Industry

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Machine Learning and Intelligent Communications (MLICOM 2017)

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Abstract

The Internet of industry (IoI) has been well advanced in modem factory accompanying with increasing application of sensor network. The industry intelligent is just basing on techniques of IoI and industry data analysis and predication. In this paper, both the training process, in which the modeling between the input of environment and technological parameters and the output of key performance index (KPI) is built, and the functional process, where the model built in training process and current is used for KPI predication. Both multivariable linear regression and nonlinear BP neural network model are employed and verified with authentic data set.

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Acknowledgement

This work was supported by the National Natural Science Foundation of China under Grant No. 61401120.

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Authors and Affiliations

  1. Communication Research Center, Harbin Institute of Technology, Harbin, 150080, China

    Haowei Li, Liming Zheng & Gang Wang

  2. Management School, Harbin Institute of Technology, Harbin, 150080, China

    Yue Wu

Authors
  1. Haowei Li
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  2. Liming Zheng
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  3. Yue Wu
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  4. Gang Wang
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Corresponding author

Correspondence to Yue Wu .

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Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Xuemai Gu

  2. Harbin Institute of Technology, Weihai, Heilongjiang, China

    Gongliang Liu

  3. Shandong University, Weihai, Heilongjiang, China

    Bo Li

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Li, H., Zheng, L., Wu, Y., Wang, G. (2018). Machine Learning Based Key Performance Index Prediction Methods in Internet of Industry. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-319-73447-7_53

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  • DOI: https://doi.org/10.1007/978-3-319-73447-7_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73446-0

  • Online ISBN: 978-3-319-73447-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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