With the vigorous expansion of nonlinear adaptive filtering with real-valued kernel functions,its counterpart complex kernel adaptive filtering algorithms were also sequentially proposed to solve the complex-valued no...With the vigorous expansion of nonlinear adaptive filtering with real-valued kernel functions,its counterpart complex kernel adaptive filtering algorithms were also sequentially proposed to solve the complex-valued nonlinear problems arising in almost all real-world applications.This paper firstly presents two schemes of the complex Gaussian kernel-based adaptive filtering algorithms to illustrate their respective characteristics.Then the theoretical convergence behavior of the complex Gaussian kernel least mean square（LMS） algorithm is studied by using the fixed dictionary strategy.The simulation results demonstrate that the theoretical curves predicted by the derived analytical models consistently coincide with the Monte Carlo simulation results in both transient and steady-state stages for two introduced complex Gaussian kernel LMS algonthms using non-circular complex data.The analytical models are able to be regard as a theoretical tool evaluating ability and allow to compare with mean square error（MSE） performance among of complex kernel LMS（KLMS） methods according to the specified kernel bandwidth and the length of dictionary.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
基金supported by the National Natural Science Foundation of China(6100115361271415+4 种基金6140149961531015)the Fundamental Research Funds for the Central Universities(3102014JCQ010103102014ZD0041)the Opening Research Foundation of State Key Laboratory of Underwater Information Processing and Control(9140C231002130C23085)
文摘With the vigorous expansion of nonlinear adaptive filtering with real-valued kernel functions,its counterpart complex kernel adaptive filtering algorithms were also sequentially proposed to solve the complex-valued nonlinear problems arising in almost all real-world applications.This paper firstly presents two schemes of the complex Gaussian kernel-based adaptive filtering algorithms to illustrate their respective characteristics.Then the theoretical convergence behavior of the complex Gaussian kernel least mean square（LMS） algorithm is studied by using the fixed dictionary strategy.The simulation results demonstrate that the theoretical curves predicted by the derived analytical models consistently coincide with the Monte Carlo simulation results in both transient and steady-state stages for two introduced complex Gaussian kernel LMS algonthms using non-circular complex data.The analytical models are able to be regard as a theoretical tool evaluating ability and allow to compare with mean square error（MSE） performance among of complex kernel LMS（KLMS） methods according to the specified kernel bandwidth and the length of dictionary.
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。
