可靠准确地预测动力电池剩余使用寿命(remaining useful life,RUL)可以缓解用户对里程和安全的焦虑。为了提升RUL预测精度,基于NASA数据集,本工作提出了一种改进的灰狼算法来优化高斯过程回归(Gaussian process regression,GPR)模型。...可靠准确地预测动力电池剩余使用寿命(remaining useful life,RUL)可以缓解用户对里程和安全的焦虑。为了提升RUL预测精度,基于NASA数据集,本工作提出了一种改进的灰狼算法来优化高斯过程回归(Gaussian process regression,GPR)模型。本工作从以下三方面开展研究。首先,基于电池的充放电数据,提取了五种间接健康因子,包括充电电压饱和间隔(CVSI,HI1)、充电峰值温度间隔(CPTI,HI2)、恒流充电间隔(CCCI,HI3)、放电峰值温度区间(DPTI,HI4)和放电恒流间隔(DCCI,HI5),并采用灰色关联方法分析健康因子和容量的相关性。其次,本工作选取GPR方法作为动力电池RUL预测模型,针对传统模型参数辨识已陷入局部最优问题,提出了基于差分算法改进的灰狼算法,提升模型预测能力。最后,利用NASA数据集对本工作所提方法进行验证。实验结果表明,所提算法预测RUL误差控制在2%以内。展开更多
High-precision filtering estimation is one of the key techniques for strapdown inertial navigation system/global navigation satellite system(SINS/GNSS)integrated navigation system,and its estimation plays an important...High-precision filtering estimation is one of the key techniques for strapdown inertial navigation system/global navigation satellite system(SINS/GNSS)integrated navigation system,and its estimation plays an important role in the performance evaluation of the navigation system.Traditional filter estimation methods usually assume that the measurement noise conforms to the Gaussian distribution,without considering the influence of the pollution introduced by the GNSS signal,which is susceptible to external interference.To address this problem,a high-precision filter estimation method using Gaussian process regression(GPR)is proposed to enhance the prediction and estimation capability of the unscented quaternion estimator(USQUE)to improve the navigation accuracy.Based on the advantage of the GPR machine learning function,the estimation performance of the sliding window for model training is measured.This method estimates the output of the observation information source through the measurement window and realizes the robust measurement update of the filter.The combination of GPR and the USQUE algorithm establishes a robust mechanism framework,which enhances the robustness and stability of traditional methods.The results of the trajectory simulation experiment and SINS/GNSS car-mounted tests indicate that the strategy has strong robustness and high estimation accuracy,which demonstrates the effectiveness of the proposed method.展开更多
Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance o...Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance of Gaussian process model.However,the common-used algorithm has the disadvantages of difficult determination of iteration steps,over-dependence of optimization effect on initial values,and easily falling into local optimum.To solve this problem,a method combining the Gaussian process with memetic algorithm was proposed.Based on this method,memetic algorithm was used to search the optimal hyper parameters of Gaussian process regression(GPR)model in the training process and form MA-GPR algorithms,and then the model was used to predict and test the results.When used in the marine long-range precision strike system(LPSS)battle effectiveness evaluation,the proposed MA-GPR model significantly improved the prediction accuracy,compared with the conjugate gradient method and the genetic algorithm optimization process.展开更多
高斯过程回归(Gaussian process regression,GPR)是一种基于高斯过程的非参数化贝叶斯回归方法,其可以灵活适应不同类型数据,用于建模和预测数据之间的复杂关系,具有拟合能力强、泛化能力好等特点。针对海量用户场景下用户量实时预测问...高斯过程回归(Gaussian process regression,GPR)是一种基于高斯过程的非参数化贝叶斯回归方法,其可以灵活适应不同类型数据,用于建模和预测数据之间的复杂关系,具有拟合能力强、泛化能力好等特点。针对海量用户场景下用户量实时预测问题,提出一种基于GPR的用户量预测优化方法。在滑动窗口方法处理数据的基础上,选择合适的核函数,基于k折交叉验证得到最佳超参数组合以实现GPR模型训练,完成在线用户量的实时预测并进行性能评估。实验结果表明,相比于采用训练集中输出数据方差的50%作为信号噪声估计量的传统方案,所提方法具有较高的预测准确度,并且在测试集均方根误差(root mean square,RMS)、平均绝对误差(mean absolute error,MAE)、平均偏差(mean bias error,MBE)和决定系数R 2这4个评估指标方面均有提升,其中MBE至少提升了43.3%。展开更多
在以太阳作为目标源的天文测速导航中,多普勒速度量测量存在较多野值误差,严重影响导航精度。对此,提出一种基于高斯过程回归与无迹卡尔曼滤波(Gaussian process regression and unscented Kalman filtering,GPR-UKF)的野值检测修复方法...在以太阳作为目标源的天文测速导航中,多普勒速度量测量存在较多野值误差,严重影响导航精度。对此,提出一种基于高斯过程回归与无迹卡尔曼滤波(Gaussian process regression and unscented Kalman filtering,GPR-UKF)的野值检测修复方法,建立速度量测量的动态预测模型。此外,还针对不同参数对模型精度的影响进行研究。经仿真验证,所提方法效果显著优于传统野值处理方法。展开更多
文摘可靠准确地预测动力电池剩余使用寿命(remaining useful life,RUL)可以缓解用户对里程和安全的焦虑。为了提升RUL预测精度,基于NASA数据集,本工作提出了一种改进的灰狼算法来优化高斯过程回归(Gaussian process regression,GPR)模型。本工作从以下三方面开展研究。首先,基于电池的充放电数据,提取了五种间接健康因子,包括充电电压饱和间隔(CVSI,HI1)、充电峰值温度间隔(CPTI,HI2)、恒流充电间隔(CCCI,HI3)、放电峰值温度区间(DPTI,HI4)和放电恒流间隔(DCCI,HI5),并采用灰色关联方法分析健康因子和容量的相关性。其次,本工作选取GPR方法作为动力电池RUL预测模型,针对传统模型参数辨识已陷入局部最优问题,提出了基于差分算法改进的灰狼算法,提升模型预测能力。最后,利用NASA数据集对本工作所提方法进行验证。实验结果表明,所提算法预测RUL误差控制在2%以内。
基金supported by the National Natural Science Foundation of China(61873275,61703419,425317829).
文摘High-precision filtering estimation is one of the key techniques for strapdown inertial navigation system/global navigation satellite system(SINS/GNSS)integrated navigation system,and its estimation plays an important role in the performance evaluation of the navigation system.Traditional filter estimation methods usually assume that the measurement noise conforms to the Gaussian distribution,without considering the influence of the pollution introduced by the GNSS signal,which is susceptible to external interference.To address this problem,a high-precision filter estimation method using Gaussian process regression(GPR)is proposed to enhance the prediction and estimation capability of the unscented quaternion estimator(USQUE)to improve the navigation accuracy.Based on the advantage of the GPR machine learning function,the estimation performance of the sliding window for model training is measured.This method estimates the output of the observation information source through the measurement window and realizes the robust measurement update of the filter.The combination of GPR and the USQUE algorithm establishes a robust mechanism framework,which enhances the robustness and stability of traditional methods.The results of the trajectory simulation experiment and SINS/GNSS car-mounted tests indicate that the strategy has strong robustness and high estimation accuracy,which demonstrates the effectiveness of the proposed method.
基金Project(513300303)supported by the General Armament Department,China
文摘Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance of Gaussian process model.However,the common-used algorithm has the disadvantages of difficult determination of iteration steps,over-dependence of optimization effect on initial values,and easily falling into local optimum.To solve this problem,a method combining the Gaussian process with memetic algorithm was proposed.Based on this method,memetic algorithm was used to search the optimal hyper parameters of Gaussian process regression(GPR)model in the training process and form MA-GPR algorithms,and then the model was used to predict and test the results.When used in the marine long-range precision strike system(LPSS)battle effectiveness evaluation,the proposed MA-GPR model significantly improved the prediction accuracy,compared with the conjugate gradient method and the genetic algorithm optimization process.
文摘高斯过程回归(Gaussian process regression,GPR)是一种基于高斯过程的非参数化贝叶斯回归方法,其可以灵活适应不同类型数据,用于建模和预测数据之间的复杂关系,具有拟合能力强、泛化能力好等特点。针对海量用户场景下用户量实时预测问题,提出一种基于GPR的用户量预测优化方法。在滑动窗口方法处理数据的基础上,选择合适的核函数,基于k折交叉验证得到最佳超参数组合以实现GPR模型训练,完成在线用户量的实时预测并进行性能评估。实验结果表明,相比于采用训练集中输出数据方差的50%作为信号噪声估计量的传统方案,所提方法具有较高的预测准确度,并且在测试集均方根误差(root mean square,RMS)、平均绝对误差(mean absolute error,MAE)、平均偏差(mean bias error,MBE)和决定系数R 2这4个评估指标方面均有提升,其中MBE至少提升了43.3%。
文摘在以太阳作为目标源的天文测速导航中,多普勒速度量测量存在较多野值误差,严重影响导航精度。对此,提出一种基于高斯过程回归与无迹卡尔曼滤波(Gaussian process regression and unscented Kalman filtering,GPR-UKF)的野值检测修复方法,建立速度量测量的动态预测模型。此外,还针对不同参数对模型精度的影响进行研究。经仿真验证,所提方法效果显著优于传统野值处理方法。
