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
In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in paral...In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm.展开更多
In order to meet the demand of online optimal running, a novel soft sensor modeling approach based on Gaussian processes was proposed. The approach is moderately simple to implement and use without loss of performance...In order to meet the demand of online optimal running, a novel soft sensor modeling approach based on Gaussian processes was proposed. The approach is moderately simple to implement and use without loss of performance. It is trained by optimizing the hyperparameters using the scaled conjugate gradient algorithm with the squared exponential covariance function employed. Experimental simulations show that the soft sensor modeling approach has the advantage via a real-world example in a refinery. Meanwhile, the method opens new possibilities for application of kernel methods to potential fields.展开更多
An effective maintenance policy optimization model can reduce maintenance cost and system operation risk. For mission-oriented systems, the degradation process changes dynamically and is monotonous and irreversible. M...An effective maintenance policy optimization model can reduce maintenance cost and system operation risk. For mission-oriented systems, the degradation process changes dynamically and is monotonous and irreversible. Meanwhile, the risk of early failure is high. Therefore, this paper proposes a dynamic condition-based maintenance(CBM) optimization model for mission-oriented system based on inverse Gaussian(IG) degradation process. Firstly, the IG process with random drift coefficient is used to describe the degradation process and the relevant probability distributions are obtained. Secondly, the dynamic preventive maintenance threshold(DPMT) function is used to control the early failure risk of the mission-oriented system, and the influence of imperfect preventive maintenance(PM)on the degradation amount and degradation rate is analysed comprehensively. Thirdly, according to the mission availability requirement, the probability formulas of different types of renewal policies are obtained, and the CBM optimization model is constructed. Finally, a numerical example is presented to verify the proposed model. The comparison with the fixed PM threshold model and the sensitivity analysis show the effectiveness and application value of the optimization model.展开更多
针对柴油发动机推进特性下的中高负荷工况出现的NO_(x)排放峰值现象,以及燃油价格日益上涨带来降低油耗率的迫切需求,本研究通过调节柴油/甲醇组合燃烧(diesel/methanol compound combustion,DMCC)发动机多种控制参数,在保证动力性前提...针对柴油发动机推进特性下的中高负荷工况出现的NO_(x)排放峰值现象,以及燃油价格日益上涨带来降低油耗率的迫切需求,本研究通过调节柴油/甲醇组合燃烧(diesel/methanol compound combustion,DMCC)发动机多种控制参数,在保证动力性前提下,实现NO_(x)排放和有效燃油消耗率(brake specific fuel consumption,BSFC)的同步下降。为避免大规模试验带来的成本增加,首先基于高斯过程回归建立DMCC发动机排放的NO_(x)体积分数、BSFC和指示功率预测模型;然后将所建模型与第二代非支配排序遗传算法(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)结合,对NO_(x)的体积分数和BSFC进行优化,并将Pareto前沿解集代入逼近理想解排序法(the technique for order preference by similarity to an ideal solution,TOPSIS)寻找最优控制参数组合;最后将最优控制参数组合标定至电子控制单元,与原机数据进行对比分析。结果表明:基于高斯过程回归建立的预测模型的拟合优度大于0.95,均方根误差小于1,具有良好的一致性和准确性;使用NSGA-Ⅱ获取的最佳控制参数与优化前(原机工况)的相比,NO_(x)的排放量下降74.5%,仅为3.47 g/(kW·h),BSFC平均下降6.7%,仅为203.5 g/(kW·h)。展开更多
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.
基金Project(2009CB320603)supported by the National Basic Research Program of ChinaProject(IRT0712)supported by Program for Changjiang Scholars and Innovative Research Team in University+1 种基金Project(B504)supported by the Shanghai Leading Academic Discipline ProgramProject(61174118)supported by the National Natural Science Foundation of China
文摘In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm.
文摘In order to meet the demand of online optimal running, a novel soft sensor modeling approach based on Gaussian processes was proposed. The approach is moderately simple to implement and use without loss of performance. It is trained by optimizing the hyperparameters using the scaled conjugate gradient algorithm with the squared exponential covariance function employed. Experimental simulations show that the soft sensor modeling approach has the advantage via a real-world example in a refinery. Meanwhile, the method opens new possibilities for application of kernel methods to potential fields.
基金supported by the National Natural Science Foundation of China (71901216)。
文摘An effective maintenance policy optimization model can reduce maintenance cost and system operation risk. For mission-oriented systems, the degradation process changes dynamically and is monotonous and irreversible. Meanwhile, the risk of early failure is high. Therefore, this paper proposes a dynamic condition-based maintenance(CBM) optimization model for mission-oriented system based on inverse Gaussian(IG) degradation process. Firstly, the IG process with random drift coefficient is used to describe the degradation process and the relevant probability distributions are obtained. Secondly, the dynamic preventive maintenance threshold(DPMT) function is used to control the early failure risk of the mission-oriented system, and the influence of imperfect preventive maintenance(PM)on the degradation amount and degradation rate is analysed comprehensively. Thirdly, according to the mission availability requirement, the probability formulas of different types of renewal policies are obtained, and the CBM optimization model is constructed. Finally, a numerical example is presented to verify the proposed model. The comparison with the fixed PM threshold model and the sensitivity analysis show the effectiveness and application value of the optimization model.
文摘多环芳烃(polycyclic aromatic hydrocarbons,PAHs)是地下水中的主要有机污染物之一,地下水中多环芳烃运移数值模拟在开展地下水污染高效修复中起重要作用。在实际地下水污染条件下,由于难以准确刻画含水介质中的胶体类型及其分布,通常忽略污染物-胶体共运移机制,建立的模型存在结构误差,导致模型预测具有显著偏差。本研究以荧蒽和菲为研究对象,针对忽略的PAHs-胶体的共运移机制,使用高斯过程回归(Gaussian process regression,GPR)修正模型结构误差,建立耦合数据驱动和物理机制的多环芳烃运移模型。通过饱和砂柱PAHs运移室内试验,对比分析了未耦合和耦合数据驱动方法的模型预测结果。结果表明,忽略PAHs-胶体的共运移机制的地下水多环芳烃运移模型具有显著的模型结构误差,直接进行参数识别不能弥补忽略的共运移机制,预测结果存在显著偏差。使用GPR模型可以有效补偿PAHs-胶体的共运移机制,修正地下水模型的结构误差。验证期荧蒽、菲预测结果的95%置信区间对观测数据的覆盖率分别提升了56.84%和19.04%,纳什系数分别提升了40.09%和21.73%,均方根误差分别降低了33.10%和55.38%,平均绝对误差分别降低了32.00%和46.34%,地下水多环芳烃运移模型的预测性能显著提高。本研究提出的耦合数据驱动和物理机制方法为场地地下水多环芳烃运移精准模拟提供了可行思路,有助于实现地下水污染的精准高效修复。
文摘针对柴油发动机推进特性下的中高负荷工况出现的NO_(x)排放峰值现象,以及燃油价格日益上涨带来降低油耗率的迫切需求,本研究通过调节柴油/甲醇组合燃烧(diesel/methanol compound combustion,DMCC)发动机多种控制参数,在保证动力性前提下,实现NO_(x)排放和有效燃油消耗率(brake specific fuel consumption,BSFC)的同步下降。为避免大规模试验带来的成本增加,首先基于高斯过程回归建立DMCC发动机排放的NO_(x)体积分数、BSFC和指示功率预测模型;然后将所建模型与第二代非支配排序遗传算法(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)结合,对NO_(x)的体积分数和BSFC进行优化,并将Pareto前沿解集代入逼近理想解排序法(the technique for order preference by similarity to an ideal solution,TOPSIS)寻找最优控制参数组合;最后将最优控制参数组合标定至电子控制单元,与原机数据进行对比分析。结果表明:基于高斯过程回归建立的预测模型的拟合优度大于0.95,均方根误差小于1,具有良好的一致性和准确性;使用NSGA-Ⅱ获取的最佳控制参数与优化前(原机工况)的相比,NO_(x)的排放量下降74.5%,仅为3.47 g/(kW·h),BSFC平均下降6.7%,仅为203.5 g/(kW·h)。
