1) recursive least squares
递归最小二乘
1.
The proposed algorithm has a tracking ability comparable to that of convention- al recursive least squares(CRLS)algorithm and is numerically stable.
新算法用最小二乘误差(LSE)代替了均方误差(MSE)作为代价函数,它具有和常规递归最小二乘(CRLS)算法相近似的追踪能力,且不存在数值计算不稳定性的问题,在收敛速度以及稳态效果方面也要优于De Campos的拟牛顿(QN)算法。
2.
The proposed algorithm has almost as good a tracking ability as that of the conventional recursive least squares(CRLS)algorithm and is numerically stable.
新算法用最小二乘误差(LSE)代替了均方误差(MSE)作为代价函数,它具有和常规递归最小二乘(CRLS)算法相近似的追踪能力,且不存在数值计算不稳定性的问题。
3.
Then a state-action value function in learning system is assumed as the linearly weighted sum of the given geodesic Gaussian basis functions,and a recursive least squares method is used to update the weights in an on-line and incremental manner.
然后,假定强化学习系统的状态—动作值函数是给定测地高斯基函数的加权组合,采用递归最小二乘方法对权值进行在线增量式更新。
2) RLS
递归最小二乘
1.
The modified recursion lest squares (RLS) channel estimation method exploits preamble training sequences and adaptive forgetting factor to estimate channel state parameters without any prior statistics knowledge of channel in multiple input multiple outputorthogonal frequency division multiplexing (MIMO-OFDM) systems.
MIMO-OFDM系统中一种改进的递归最小二乘(RLS)信道估计方法可以在不需要任何信道统计信息的前提下,利用前导训练序列和自适应遗忘因子对信道状态参数进行估计。
2.
In order to speed the convergence rate of the existing RLS prediction in time-varying channels, an improved adaptive RLS prediction for single carrier-frequency domain equalization (SC-FDE) system was proposed.
针对现有的时变信道递归最小二乘(RLS)预测算法收敛慢的问题提出了一种单载波频域均衡(singlecarrier–frequency domain equalization,SC-FDE)系统中改进的自适应RLS预测算法。
3.
The computer simulation shows that the RLS-SIC is better than the LMS-SIC at the multiple access interference(MAI) and near-far scenarios.
通过计算机仿真可知,采用递归最小二乘的自适应串行干扰消除器比最小均方自适应串行干扰消除器在抗多址和抗远近效应方面性能要更胜一筹。
3) recursive least square
递归最小二乘
1.
Aiming at the lower performance and floor effect of the traditional orthogonal frequency division multiplexing channel estimation,the OFDM channel estimation algorithm based on wavelet recursive least square support vector machine (WRLS-SVM) is proposed.
针对传统多径衰落下的OFDM导频信道估计性能低下,地板效应的缺陷,提出了基于导频的小波递归最小二乘支持向量机(WRLS-SVM)时变信道频率估计算法。
2.
A new one-dimensional(1D) recursive least square(RLS) adaptive channel estimation algorithm was proposed in time domain.
分析了正交频分复用(OFDM)信道估计中的信道冲激响应(CIR)泄漏问题,提出了新的时域一维递归最小二乘(RLS)自适应信道估计算法。
3.
Then after selecting the reliable counterpart of tracks reported by diverse radars as the prior information,the bias estimation can be eventually made by a recursive least square estimator and.
该方法利用对雷达系统偏差不敏感的新特征量———目标参照拓扑对多雷达航迹进行自适应的预关联,然后根据关联质量选择可靠的关联航迹对作为雷达系统偏差估计的先验信息,最后应用递归最小二乘算法进行偏差估计,估计结果可为预关联过程提供依据。
4) RLS
递归最小二乘法
1.
In this paper,a novel channel tracking algorithm based on Recursive Least Squares(RLS-IC) is proposed for Multi-input multi-output(MIMO) wireless communication systems under frequency flat fading with time variation environment.
该文提出了时变平坦衰落信道环境下多输入多输出(M IMO)通信系统中一种新的基于递归最小二乘法(RLS)的带干扰对消的信道跟踪方法(RLS-IC)。
2.
A new algorithm RLS will be u-tilized in order to study and improve the performance of the DPSK demodulation method.
采用常用的递归最小二乘法(RLS)自适应算法,研究了自适应解调方法对DPSK信号的解调及其性能。
5) recursive least square
递归最小二乘方
6) recursive least squares method
递归最小二乘法
1.
The recursive least squares method was applied to forecast the tangential force coefficient and slip curve slope.
为避免这种现象的产生,建立了制动气缸压力的非线性模型,利用干扰观测器对黏着系数进行估计,运用递归最小二乘法预测切向力系数与滑移率关系曲线的斜率,采用滑模变结构与逻辑门限值相结合的控制方法对系统进行控制。
2.
And recursive least squares method was used to forecast adhesion-slip curve slope.
为避免这种现象的产生,建立轮轨牵引力矩传递的简化模型,利用干扰观测器对粘着系数以及车辆速度进行估计,运用递归最小二乘法预测切线力系数与相对滑动速度曲线的斜率,以判断当前状态是否滑动,采用模糊PID控制算法对系统进行控制。
补充资料：非线性最小二乘拟合
分子式：
CAS号：
性质：用最小二乘法拟合非线性方程。有些变量之间的非线性模型，通过变量变换可以化为线性模型，此称为外在线性。而有些变量之间的非线性模型，通过变量变换不能化为线性模型，通称为内在非线性。对于非线性模型y=f(ξ,θ)+ε，其残差平方和。S(θ)是θ的函数，当模型关于θ是非线性的，正规方程关于θ也是非线性的。基于使残差平方和s(θ)达到极小的原理求出θ的估计值，拟合非线性回归方程。
CAS号：
性质：用最小二乘法拟合非线性方程。有些变量之间的非线性模型，通过变量变换可以化为线性模型，此称为外在线性。而有些变量之间的非线性模型，通过变量变换不能化为线性模型，通称为内在非线性。对于非线性模型y=f(ξ,θ)+ε，其残差平方和。S(θ)是θ的函数，当模型关于θ是非线性的，正规方程关于θ也是非线性的。基于使残差平方和s(θ)达到极小的原理求出θ的估计值，拟合非线性回归方程。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条