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Distributed Intelligent Pension System Based on BP Neural Network

  • Xujia Wang
  • Dong Liang
  • Wei Song
  • Yong Zhou
Article
  • 33 Downloads

Abstract

The distributed intelligent pension system is a new old-age pension system that is designed to solve the problem existed in decentralized management system in traditional nursing homes, such as information isolation and imperfect pension facilities. The system combines the advantages of RFID technology and video linkage monitoring. In order to know whether the elderly is well taken care of, the two types of information need to be processed and analyzed. Data fusion technology is an effective tool to solve the optimal decision of multi attribute data. In the algorithm of data fusion, the neural network algorithm has good fault tolerance and adaptability, and requires a small priori probability distribution of the system. It can handle incomplete and inaccurate information. Combined with the multi-source and massive characteristics of the data of distributed intelligent pension system, the data processing has the characteristics of real-time and accuracy. In addition, the BP neural network has the characteristics of simple realization and high recognition precision in a certain range. The BP neural network algorithm is used as the research, and the additional momentum method is used to improve the traditional BP algorithm. In the same direction, the gradient is added to the weight and threshold, and the algorithm is guaranteed to the direction of convergence.

Keywords

Distributed intelligent pension system Data fusion Rough set BP neural network 

Notes

Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant: 51707154), Humanity and Social Science Youth foundation of Ministry of Education of China (Grant: 17YJC790128). Ministry of Education, Humanities and Social Sciences Project (Grant: 14YJA790090).

References

  1. 1.
    Gao, Y., Qu, C., & Zhang, K. (2016). A hybrid method based on singular spectrum analysis, firefly algorithm, and bp neural network for short-term wind speed forecasting. Energies, 9(1–10), 757.CrossRefGoogle Scholar
  2. 2.
    Zhao, Z., Xu, Q., & Jia, M. (2016). Improved shuffled frog leaping algorithm-based bp neural network and its application in bearing early fault diagnosis. Neural Computing and Applications, 27(2), 375–385.CrossRefGoogle Scholar
  3. 3.
    Lu, Y., Lu, G., Bu, X., & Yu, Y. (2015). Classification of hand manipulation using bp neural network and support vector machine based on surface electromyography signal*. Ifac Papersonline, 48(28), 869–873.CrossRefGoogle Scholar
  4. 4.
    Qiu, C., & Shan, J. (2015). Research on intrusion detection algorithm based on bp neural network. International Journal of Security & Its Applications, 9(4), 247–258.CrossRefGoogle Scholar
  5. 5.
    Ma, J., Yu, J., Hao, G., Wang, D., Sun, Y., Lu, J., et al. (2017). Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model. Lipids in Health and Disease, 16(1), 42.CrossRefGoogle Scholar
  6. 6.
    Liang, L., Sun, M., Zhang, S., Wen, Y., Zhao, P., & Yuan, J. (2015). Control system design of anti-rolling tank swing bench using bp neural network pid based on labview. International Journal of Smart Home, 9(6), 1–10.CrossRefGoogle Scholar
  7. 7.
    Kulkarni, P., Londhe, S., & Deo, M. (2017). Artificial neural networks for construction management: A review. Soft Computing in Civil Engineering, 1(2), 70–88.Google Scholar
  8. 8.
    Ren, T., Liu, S., Yan, G., & Mu, H. (2016). Temperature prediction of the molten salt collector tube using bp neural network. Renewable Power Generation Iet, 10(2), 212–220.CrossRefGoogle Scholar
  9. 9.
    Jia, W., Zhao, D., Shen, T., Ding, S., Zhao, Y., & Hu, C. (2015). An optimized classification algorithm by bp neural network based on pls and hca. Applied Intelligence, 43(1), 1–16.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Xi’an University of Architecture and TechnologyXi’anChina
  2. 2.State Key Laboratory Base of Eco-hydraulic Engineering in Arid AreaXi’an University of TechnologyXi’anChina

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