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
锂电池健康状态(state of health, SOH)的退化过程在一定程度上是一个非平稳随机过程,使得当前多数点估计机器学习方法在实际应用中受到限制。基于贝叶斯理论的高斯过程回归(Gaussian process regression,GPR),因可输出估计结果的不确定...锂电池健康状态(state of health, SOH)的退化过程在一定程度上是一个非平稳随机过程,使得当前多数点估计机器学习方法在实际应用中受到限制。基于贝叶斯理论的高斯过程回归(Gaussian process regression,GPR),因可输出估计结果的不确定性,近年来在锂电池SOH区间估计中得到广泛应用。然而,GPR的性能很大程度上取决于其核函数的选择,当前研究多凭借经验选用固定单一核函数,无法适应不同的数据集。为此,本文提出一种基于自适应最优组合核函数GPR的锂电池SOH区间估计方法。该方法首先从电池充放电数据中提取出多个健康因子(health factor, HF),并采用皮尔森相关系数法优选出6个与SOH高度相关的健康因子作为模型的输入。然后,在当前常用的7个核函数集合上,通过两两随机组合构造新的组合核函数,并利用交叉验证自适应优选出最优组合核函数。采用3个不同数据集对所提方法进行了验证,结果表明:本文方法具有出色的SOH区间估计性能。在3个公开数据集上,平均区间宽度指标在0.0509以内,平均区间分数大于-0.0004,均方根误差小于0.0181。展开更多
轨迹跟踪是无人驾驶控制系统中至关重要的功能之一。车辆动力学模型对轨迹跟踪性能有显著影响,但是存在模型复杂度和求解效率之间的矛盾,在非线性工况下无法满足轨迹跟踪精度要求,为此提出基于高斯过程回归(Gaussian Process Regression...轨迹跟踪是无人驾驶控制系统中至关重要的功能之一。车辆动力学模型对轨迹跟踪性能有显著影响,但是存在模型复杂度和求解效率之间的矛盾,在非线性工况下无法满足轨迹跟踪精度要求,为此提出基于高斯过程回归(Gaussian Process Regression,GPR)的模型预测控制(Model Predictive Control,MPC)方法。使用简单模型从而确保求解效率,通过GPR对车辆模型补偿从而提高轨迹跟踪性能。提出基于单轨动力学模型的车辆状态融合估计方法,获得GPR误差补偿模型;构建轨迹跟踪问题模型,推导GPR误差补偿模型在预测时域的迭代方程,对预测时域内的车辆状态进行动态补偿,实现轨迹跟踪控制;通过搭建实车验证平台开展典型工况试验验证,与无补偿MPC方法进行对比。研究结果表明,新方法轨迹跟踪精度得到明显提升,轨迹跟踪横向误差和航向误差分别降低了33.3%和27.9%,同时还兼顾了车辆舒适性的提升,侧向加速度和横摆角速度均值分别下降了17.1%和21.7%。展开更多
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.
文摘锂电池健康状态(state of health, SOH)的退化过程在一定程度上是一个非平稳随机过程,使得当前多数点估计机器学习方法在实际应用中受到限制。基于贝叶斯理论的高斯过程回归(Gaussian process regression,GPR),因可输出估计结果的不确定性,近年来在锂电池SOH区间估计中得到广泛应用。然而,GPR的性能很大程度上取决于其核函数的选择,当前研究多凭借经验选用固定单一核函数,无法适应不同的数据集。为此,本文提出一种基于自适应最优组合核函数GPR的锂电池SOH区间估计方法。该方法首先从电池充放电数据中提取出多个健康因子(health factor, HF),并采用皮尔森相关系数法优选出6个与SOH高度相关的健康因子作为模型的输入。然后,在当前常用的7个核函数集合上,通过两两随机组合构造新的组合核函数,并利用交叉验证自适应优选出最优组合核函数。采用3个不同数据集对所提方法进行了验证,结果表明:本文方法具有出色的SOH区间估计性能。在3个公开数据集上,平均区间宽度指标在0.0509以内,平均区间分数大于-0.0004,均方根误差小于0.0181。
文摘轨迹跟踪是无人驾驶控制系统中至关重要的功能之一。车辆动力学模型对轨迹跟踪性能有显著影响,但是存在模型复杂度和求解效率之间的矛盾,在非线性工况下无法满足轨迹跟踪精度要求,为此提出基于高斯过程回归(Gaussian Process Regression,GPR)的模型预测控制(Model Predictive Control,MPC)方法。使用简单模型从而确保求解效率,通过GPR对车辆模型补偿从而提高轨迹跟踪性能。提出基于单轨动力学模型的车辆状态融合估计方法,获得GPR误差补偿模型;构建轨迹跟踪问题模型,推导GPR误差补偿模型在预测时域的迭代方程,对预测时域内的车辆状态进行动态补偿,实现轨迹跟踪控制;通过搭建实车验证平台开展典型工况试验验证,与无补偿MPC方法进行对比。研究结果表明,新方法轨迹跟踪精度得到明显提升,轨迹跟踪横向误差和航向误差分别降低了33.3%和27.9%,同时还兼顾了车辆舒适性的提升,侧向加速度和横摆角速度均值分别下降了17.1%和21.7%。
