The deferred correction(DeC)is an iterative procedure,characterized by increasing the accuracy at each iteration,which can be used to design numerical methods for systems of ODEs.The main advantage of such framework i...The deferred correction(DeC)is an iterative procedure,characterized by increasing the accuracy at each iteration,which can be used to design numerical methods for systems of ODEs.The main advantage of such framework is the automatic way of getting arbitrarily high order methods,which can be put in the Runge-Kutta(RK)form.The drawback is the larger computational cost with respect to the most used RK methods.To reduce such cost,in an explicit setting,we propose an efcient modifcation:we introduce interpolation processes between the DeC iterations,decreasing the computational cost associated to the low order ones.We provide the Butcher tableaux of the new modifed methods and we study their stability,showing that in some cases the computational advantage does not afect the stability.The fexibility of the novel modifcation allows nontrivial applications to PDEs and construction of adaptive methods.The good performances of the introduced methods are broadly tested on several benchmarks both in ODE and PDE contexts.展开更多
雷达、声呐和无线通信等应用对于自适应波束形成的抗干扰能力和实时性提出了更高的要求。传统基于最速迭代的自适应波束形成算法存在“过拟合”特性,导致在相干干扰条件下的干扰抑制性能急剧下降。另外,当干扰存在扰动且导向向量失配时...雷达、声呐和无线通信等应用对于自适应波束形成的抗干扰能力和实时性提出了更高的要求。传统基于最速迭代的自适应波束形成算法存在“过拟合”特性,导致在相干干扰条件下的干扰抑制性能急剧下降。另外,当干扰存在扰动且导向向量失配时,也无法有效抑制干扰。针对上述问题,本文提出了一种基于共轭梯度(Conjugate Gradient,CG)加速的二次约束宽零陷干扰抑制自适应波束形成方法。该方法首先利用CG算法的快速收敛特性,完成采样协方差矩阵与导向向量间线性方程组的求解;其次将CG算法输出的权矢量作为迭代最速波束形成方法的初始权值,利用该方法的“过拟合”特性,确保对期望信号的强锁定;最后提出了一种强化干扰特征的波达方向(Direction of Arrival,DOA)估计方法,实现宽带相干干扰下的干扰来波方向估计,并将该方法与二次约束零陷展宽方法结合,用于捕获干扰特征,形成自适应零陷。仿真实验验证了所提方法在单快拍、宽带相干干扰条件下,能够自适应抑制干扰且稳健性较好。展开更多
文摘The deferred correction(DeC)is an iterative procedure,characterized by increasing the accuracy at each iteration,which can be used to design numerical methods for systems of ODEs.The main advantage of such framework is the automatic way of getting arbitrarily high order methods,which can be put in the Runge-Kutta(RK)form.The drawback is the larger computational cost with respect to the most used RK methods.To reduce such cost,in an explicit setting,we propose an efcient modifcation:we introduce interpolation processes between the DeC iterations,decreasing the computational cost associated to the low order ones.We provide the Butcher tableaux of the new modifed methods and we study their stability,showing that in some cases the computational advantage does not afect the stability.The fexibility of the novel modifcation allows nontrivial applications to PDEs and construction of adaptive methods.The good performances of the introduced methods are broadly tested on several benchmarks both in ODE and PDE contexts.
文摘在高压并联电抗器声纹信号监测系统中,长时海量无标签声纹的高维非平稳性导致特征提取困难、无监督聚类适应性差。由此提出了一种基于深度自适应K-means++算法(deep adaptive K-means++clustering algorithm,DAKCA)的750 kV电抗器声纹聚类方法。首先通过采用两阶段无监督策略微调的改进堆叠稀疏自编码器(stacked sparse autoencoder,SSAE),对快速傅里叶变换后的归一化频域数据提取电抗器原始声纹32维深度特征。进一步提出了依据最近邻聚类有效性指标(clustering validation index based on nearest neighbors,CVNN)的自适应K-means++聚类算法,构建了能自适应确定最优聚类个数的电抗器声纹聚类模型。最后通过西北地区某750 kV电抗器实测声纹数据集进行了验证。结果表明,DAKCA算法对无标签声纹数据在不同样本均衡程度下能够稳定提取32维深度特征,并实现最优聚类,为直接高效利用电抗器无标签声纹数据提供了参考。
文摘雷达、声呐和无线通信等应用对于自适应波束形成的抗干扰能力和实时性提出了更高的要求。传统基于最速迭代的自适应波束形成算法存在“过拟合”特性,导致在相干干扰条件下的干扰抑制性能急剧下降。另外,当干扰存在扰动且导向向量失配时,也无法有效抑制干扰。针对上述问题,本文提出了一种基于共轭梯度(Conjugate Gradient,CG)加速的二次约束宽零陷干扰抑制自适应波束形成方法。该方法首先利用CG算法的快速收敛特性,完成采样协方差矩阵与导向向量间线性方程组的求解;其次将CG算法输出的权矢量作为迭代最速波束形成方法的初始权值,利用该方法的“过拟合”特性,确保对期望信号的强锁定;最后提出了一种强化干扰特征的波达方向(Direction of Arrival,DOA)估计方法,实现宽带相干干扰下的干扰来波方向估计,并将该方法与二次约束零陷展宽方法结合,用于捕获干扰特征,形成自适应零陷。仿真实验验证了所提方法在单快拍、宽带相干干扰条件下,能够自适应抑制干扰且稳健性较好。
