Grey Model Optimized by Particle Swarm Optimization for Data Analysis and Application of Multi-Sensors
- PMID:30071641
- PMCID: PMC6111666
- DOI: 10.3390/s18082503
Grey Model Optimized by Particle Swarm Optimization for Data Analysis and Application of Multi-Sensors
Abstract
Data on the effective operation of new pumping station is scarce, and the unit structure is complex, as the temperature changes of different parts of the unit are coupled with multiple factors. The multivariable grey system prediction model can effectively predict the multiple parameter change of a nonlinear system model by using a small amount of data, but the value of itsq parameters greatly influences the prediction accuracy of the model. Therefore, the particle swarm optimization algorithm is used to optimize theq parameters and the multi-sensor temperature data of a pumping station unit is processed. Then, the change trends of the temperature data are analyzed and predicted. Comparing the results with the unoptimized multi-variable grey model and the BP neural network prediction method trained under insufficient data conditions, it is proved that the relative error of the multi-variable grey model after optimizing theq parameters is smaller.
Keywords: multi-sensor temperature data; multivariable grey system prediction; pumping station; q parameters; temperature change.
Conflict of interest statement
The authors declare no conflict of interest.
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