This paper proposes a novel recursive partitioning method based on constrained learning neural networks to find an arbitrary number (less than the order of the polynomial) of (real or complex) roots of arbitrary polyn...This paper proposes a novel recursive partitioning method based on constrained learning neural networks to find an arbitrary number (less than the order of the polynomial) of (real or complex) roots of arbitrary polynomials. Moreover, this paper also gives a BP network constrained learning algorithm (CLA) used in root-finders based on the constrained relations between the roots and the coefficients of polynomials. At the same time, an adaptive selection method for the parameter d P with the CLA is also given. The experimental results demonstrate that this method can more rapidly and effectively obtain the roots of arbitrary high order polynomials with higher precision than traditional root-finding approaches.展开更多
针对群组移动节点定位算法普遍基于不切实际的假设,存在普适性欠佳和精度不高的问题,提出一种基于运动参数预测的群组移动节点定位算法。该算法根据群组移动节点具有相似运动的特点,运用Hermite插值多项式预测、过滤节点运动参数。为确...针对群组移动节点定位算法普遍基于不切实际的假设,存在普适性欠佳和精度不高的问题,提出一种基于运动参数预测的群组移动节点定位算法。该算法根据群组移动节点具有相似运动的特点,运用Hermite插值多项式预测、过滤节点运动参数。为确保定位精度,应对节点移动性带来的采样区域变化,运用预测节点运动参数构建粒子有效采样区域;为节省时间开销,基于采样粒子真实分布与其极大似然估计值之间的最大K-L(Kullback-Leibler)距离确定能够满足不同采样区域的最少粒子数目;为改善算法收敛性,运用预测运动参数创建滤波公式,并选取优质粒子参与节点位置估计。在与经典算法MCL(Monte Carlo localization)法和加权最小二乘法的MATLAB对比实验中,分析了节点移动速度、自由度、K-L距离阈值、采样方格边长对定位精度的影响。结果表明,较上述算法,本算法的定位误差和时间开销较小,无须锚节点辅助,普适性较好。展开更多
文摘This paper proposes a novel recursive partitioning method based on constrained learning neural networks to find an arbitrary number (less than the order of the polynomial) of (real or complex) roots of arbitrary polynomials. Moreover, this paper also gives a BP network constrained learning algorithm (CLA) used in root-finders based on the constrained relations between the roots and the coefficients of polynomials. At the same time, an adaptive selection method for the parameter d P with the CLA is also given. The experimental results demonstrate that this method can more rapidly and effectively obtain the roots of arbitrary high order polynomials with higher precision than traditional root-finding approaches.
文摘针对群组移动节点定位算法普遍基于不切实际的假设,存在普适性欠佳和精度不高的问题,提出一种基于运动参数预测的群组移动节点定位算法。该算法根据群组移动节点具有相似运动的特点,运用Hermite插值多项式预测、过滤节点运动参数。为确保定位精度,应对节点移动性带来的采样区域变化,运用预测节点运动参数构建粒子有效采样区域;为节省时间开销,基于采样粒子真实分布与其极大似然估计值之间的最大K-L(Kullback-Leibler)距离确定能够满足不同采样区域的最少粒子数目;为改善算法收敛性,运用预测运动参数创建滤波公式,并选取优质粒子参与节点位置估计。在与经典算法MCL(Monte Carlo localization)法和加权最小二乘法的MATLAB对比实验中,分析了节点移动速度、自由度、K-L距离阈值、采样方格边长对定位精度的影响。结果表明,较上述算法,本算法的定位误差和时间开销较小,无须锚节点辅助,普适性较好。
