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.
Similar content being viewed by others
References
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.
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.
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.
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.
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.
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.
Kulkarni, P., Londhe, S., & Deo, M. (2017). Artificial neural networks for construction management: A review. Soft Computing in Civil Engineering, 1(2), 70–88.
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.
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.
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).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, X., Liang, D., Song, W. et al. Distributed Intelligent Pension System Based on BP Neural Network. Wireless Pers Commun 102, 3603–3614 (2018). https://doi.org/10.1007/s11277-018-5394-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-018-5394-1