Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11277-018-5394-1 | Wireless Personal Communications |
Keywords | Field | DocType |
Distributed intelligent pension system,Data fusion,Rough set,BP neural network | Convergence (routing),A priori probability,Data mining,Data processing,Information needs,Optimal decision,Computer science,Real-time computing,Sensor fusion,Fault tolerance,Artificial neural network | Journal |
Volume | Issue | ISSN |
102 | 4 | 0929-6212 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xu-Jia Wang | 1 | 1 | 1.45 |
| Dong Liang | 2 | 0 | 0.34 |
| Wei Song | 3 | 113 | 15.51 |
| Yong Zhou | 4 | 13 | 5.54 |