A novel neural network based on iterated unscented Kalman filter （IUKF） algorithm is established to model and com- pensate for the fiber optic gyro （FOG） bias drift caused by temperature. In the network, FOG tempe...A novel neural network based on iterated unscented Kalman filter （IUKF） algorithm is established to model and com- pensate for the fiber optic gyro （FOG） bias drift caused by temperature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter （UKF）. The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measure- ment noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and mea- surement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise （-20-20℃） and drop （70-20℃） tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the back- propagation （BP） and UKF network models.展开更多
为了探究不同长度的玄武岩纤维(BF)共同作用时对磷建筑石膏基复合材料(PBGCM)的影响,在磷建筑石膏中加入不同长度的混杂BF,得到混杂纤维增强PBGCM。通过正交试验,从直观分析、极差分析、方差分析对其力学性能和工作性能及不同纤维长度...为了探究不同长度的玄武岩纤维(BF)共同作用时对磷建筑石膏基复合材料(PBGCM)的影响,在磷建筑石膏中加入不同长度的混杂BF,得到混杂纤维增强PBGCM。通过正交试验,从直观分析、极差分析、方差分析对其力学性能和工作性能及不同纤维长度的协同作用机理进行了研究。结果表明:3 mm和15 mm BF混掺对PBGCM的力学性能增强效果往往比3 mm和15 mm BF单掺更好;PBGCM的最大绝干抗压强度、绝干抗拉强度、绝干抗折强度分别比未掺纤维的磷建筑石膏高50.10%、122.15%、79.43%;3 mm和15 mm BF混掺对PBGCM的工作性能减弱效果小于15 mm BF单独加入。3 mm BF对PBGCM的力学性能和工作性能都有极显著的影响;15 mm BF除对其绝干抗压强度没有显著影响外,对其他性能均产生极显著的影响,且影响效果比3 mm BF更大。展开更多
基金supported by the National Natural Science Foundation of China(6110418440904018)+3 种基金the National Key Scientific Instrument and Equipment Development Project(2011YQ12004502)the Research Foundation of General Armament Department(201300000008)the Doctor Innovation Fund of Naval University of Engineering(HGBSCXJJ2011008)the Youth Natural Science Foundation of Naval University of Engineering(HGDQNJJ12028)
文摘A novel neural network based on iterated unscented Kalman filter （IUKF） algorithm is established to model and com- pensate for the fiber optic gyro （FOG） bias drift caused by temperature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter （UKF）. The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measure- ment noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and mea- surement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise （-20-20℃） and drop （70-20℃） tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the back- propagation （BP） and UKF network models.
文摘为了探究不同长度的玄武岩纤维(BF)共同作用时对磷建筑石膏基复合材料(PBGCM)的影响,在磷建筑石膏中加入不同长度的混杂BF,得到混杂纤维增强PBGCM。通过正交试验,从直观分析、极差分析、方差分析对其力学性能和工作性能及不同纤维长度的协同作用机理进行了研究。结果表明:3 mm和15 mm BF混掺对PBGCM的力学性能增强效果往往比3 mm和15 mm BF单掺更好;PBGCM的最大绝干抗压强度、绝干抗拉强度、绝干抗折强度分别比未掺纤维的磷建筑石膏高50.10%、122.15%、79.43%;3 mm和15 mm BF混掺对PBGCM的工作性能减弱效果小于15 mm BF单独加入。3 mm BF对PBGCM的力学性能和工作性能都有极显著的影响;15 mm BF除对其绝干抗压强度没有显著影响外,对其他性能均产生极显著的影响,且影响效果比3 mm BF更大。
