Design of BP-GA Combination Prediction Model for Signal Condition of Industrial Time-Gate Sensors
A signal condition prediction method of time-gate sensor based on BP neural network and grey model is designed.The weight-scale-average method is used to construct the corresponding weight square and mean joint model,which realizes the adaptive processing of multiple samples and significantly improves the prediction accuracy.The results show that the correlation of this test is more than 98％,and the ideal prediction accuracy is achieved.The combined model is used to obtain the test parameters very close to the forecast results,and the accurate forecast results are better than that of a single model,which can meet the actual forecast requirements and achieve high prediction accuracy.This research is helpful to improve the level of industrial automation,and has a good effect for the subsequent improvement of energy saving.
time gate sensorexcitation signal errorcombination prediction modelhealth diagnosis
NETL
NSTL
万方数据
