A Multi-innovation Recursive Least Squares Algorithm with a Forgetting Factor for Hammerstein CAR Systems with Backlash

This study addresses the identification of Hammerstein CAR systems with backlash, where the nonlinear backlash is described as one regression identification model using a two switching function mathematical model. In such a case, the Hammerstein CAR systems with backlash can be transformed into a piecewise linearized model. Then, a novel multi-innovation recursive least squares algorithm with a forgetting factor is applied to estimate the parameters of the proposed model. Finally, numerical examples are presented to test the performance of the proposed algorithm.     

最少元件的多输入多输出MOCCII电流模式滤波器

本文提出了两种基于MOCCⅡ（多端输出的第二代电流传输器）的多输入多输出的电流模式滤波器。两种电路均由2个MOCCⅡ及4个接地RC元件构成。每一种电路除了实现出单输出的低通、带通、高通、带组、全通电流模式滤波器外，还能实现三种不同类型的具有同时多输出的电流模式滤波器，提出的电路具有很低的无源灵敏度；同时应用基本电流镜技术实现出结构简单的高精度CMOS MOCCⅡ，并对MOCCⅡ及提出的滤流器电路进行了PSICE仿真。     

Least Squares-Based Iterative Identification Methods for Linear-in-Parameters Systems Using the Decomposition Technique

By extending the least squares-based iterative (LSI) method, this paper presents a decomposition-based LSI (D-LSI) algorithm for identifying linear-in-parameters systems and an interval-varying D-LSI algorithm for handling the identification problems of missing-data systems. The basic idea is to apply the hierarchical identification principle to decompose the original system into two fictitious sub-systems and then to derive new iterative algorithms to estimate the parameters of each sub-system. Compared with the LSI algorithm and the interval-varying LSI algorithm, the decomposition-based iterative algorithms have less computational load. The numerical simulation results demonstrate that the proposed algorithms work quite well.     

一种基于最小二乘法的经典辩识的新方法

系统辩识方法分为经典和现代两大类.经典辩识方法采用非参数模型,其表现形式是以时间或频率为自变量的实验曲线.在对以时间或频率为自变量的实验曲线或者实验数据进行处理时,由于传统方法本身固有的缺陷,导致辩识过程的随意性较大,不能利用所有数据,从而造成不能充分利用所测数据的全部信息,不但浪费了信息,而且辩识出来的结果准确度很差.本文提出了一种用最小二乘法解决上述问题的方法,实践证明具有较高的精度,取得了较好的辩识效果.     

Maximum Likelihood Recursive Least Squares Estimation for Multivariable Systems

This paper discusses parameter estimation problems of the multivariable systems described by input–output difference equations. We decompose a multivariable system to several subsystems according to the number of the outputs. Based on the maximum likelihood principle, a maximum likelihood-based recursive least squares algorithm is derived to estimate the parameters of each subsystem. Finally, two numerical examples are provided to verify the effectiveness of the proposed algorithm.     

Fuzzy Least Squares for Identification of Individual Pharmacokinetic Parameters

In this paper, we examined the added value of fuzzy nonlinear regression to identify individual pharmacokinetic parameters in the case of noisy fuzzy data and/or small sample sizes. We first described three approaches that use least squares of errors as a fitting criterion for parameter estimation by fuzzy regression. Next, we compared the estimation and prediction capability of fuzzy least squares (FLS) and ordinary least squares (OLS) regressions via a simulation experiment, so as to determine the conditions of data size and variability under which one approach could be deemed superior over the other. We considered two empirical pharmacokinetic models. Our results showed that OLS regression outperformed FLS regression when the sample size was larger and/or there existed more outliers in the data. Overall, FLS regression was more powerful as the dataset size decreased. When the data were smaller in size and contained more variability, FLS regression's performance remained better than that of the OLS regression. Although the accuracy of the three FLS regression approaches was very close in almost all instances, those that estimated fuzzy parameters were superior in terms of predictive capability. These findings could aid in selecting the proper regression technique to employ in the presence of fuzzy data.      

Convergence Analysis of the Hierarchical Least Squares Algorithm for Bilinear-in-Parameter Systems

This paper studies the convergence of the hierarchical identification algorithm for bilinear-in-parameter systems. By replacing the unknown variables in the information vector with their estimates, a hierarchical least squares algorithm is derived based on the model decomposition. The proposed algorithm has higher computational efficiency than the over-parameterization model-based recursive least squares algorithm. The performance analysis shows that the parameter estimation errors converge to zero under persistent excitation conditions. The effectiveness of the proposed algorithm is verified by simulation examples.     

Recursive Least Squares Parameter Estimation for a Class of Output Nonlinear Systems Based on the Model Decomposition

In this paper, we study the parameter estimation problem of a class of output nonlinear systems and propose a recursive least squares (RLS) algorithm for estimating the parameters of the nonlinear systems based on the model decomposition. The proposed algorithm has lower computational cost than the existing over-parameterization model-based RLS algorithm. The simulation results indicate that the proposed algorithm can effectively estimate the parameters of the nonlinear systems.     

A Least Squares Solution for Use in the Six-Port Measurement Technique

Although based on the use of simple amplitude detectors, it is possible to obtain complex values of reflection coefficient, via the six-port technique, from the intersection of three circles in the complex plane. In a typical case, the circle centers are detemined primarily by the six-port design and are nominally constant, while the radii are proportional to the square root of the ratio of the output of three of the detectors to a fourth one. As a practical matter, however, these circles will not intersect in a point because of noise or other errors in the detectors.This paper develops a presedure for choosing Gamma in this context. Moreover, the question of what may be inferred about the system performance from the extent of this intersection failue is briefly considered.     

基于加权最小二乘的宽带波束形成算法

宽带波束形成技术是阵列信号处理研究的一个重要方向。基阵对信号的响应特性随频率而改变导致通过基阵的宽带信号产生波形畸变。恒定束宽波束形成可以实现在信号带宽内基阵波束图主瓣宽度保持恒定。主要研究基于加权最小二乘的恒定束宽宽带数字波束形成方法及其实现,MATLAB仿真实验表明算法的正确性和有效性。     

基于最小二乘法的吸咀吸取压力的研究

表面贴装技术生产线中的电子元件实装机通过吸咀内的负压将电子元件吸附,从而实现对电子元器件的拾取和贴装。为便于准确掌握吸咀取料是否成功,通过将负压传感器检测到的压力变化与PMAC板卡上的A/D转换值进行对应并将其量化,对得到的数据利用最小二乘法的原理在MATLAB软件环境下进行多项式拟合,得到最优的数据曲线和数据结果。     

Networked Predictive Control Systems Based on the Hammerstein Model

In this paper, a novel predictive control-based approach is proposed for a networked control system with random delays containing an input nonlinear process based on a Hammerstein model. The method uses a time-delay two-step generalized predictive control scheme, which consists of two parts: one is to deal with the input nonlinearity of the Hammerstein model and the other is to compensate for the network-induced delay in the networked control system. A theoretical result using the Popov criterion is presented for the closed-loop stability of the system in the case of a constant delay. Simulation examples illustrating the validity of the approach are also presented.     

基于量子协方差成形估计器的MIMO检测方法

协方差成形最小二乘估计器是量子信号处理框架内基于MMSE协方差成形方法的一种新型线性估计器.本文将协方差成形最小二乘估计器应用于MIMO系统检测,提出了基于协方差成形最小二乘估计器的MIMO系统检测算法,通过数值计算进行性能分析,仿真结果表明协方差成形MIMO检测方法的误码率性能优于传统ZF和MMSE检测方法,同时系统性能与目标成形矩阵有关.     

基于最小二乘虚拟阵元的解模糊方法

阵列信号处理中,当阵元间距〈1/2波长时方向估计会出现模糊。文中提出一种基于最小二乘虚拟阵元的方向估计解模糊方法,该方法根据实际已知阵元的接收数据,运用最小二乘法估计出相邻阵元间虚拟阵元上的接收数据,在实际孔径不变的情况下使阵元间距减小,从而实现了方向估计的解模糊。同时,文中还对此方法在解模糊时的虚拟阵元数进行了分析。仿真实验结果表明了该方法的有效性。     

基于约束总体最小二乘的单站TDOA-FDOA定位算法

针对利用基于外辐射源的单站定位目标问题,提出了一种基于约束总体最小二乘(CTLS)的TDOA-FDOA联合定位算法.首先构建TDOA和FDOA的观测方程,通过引入目标到观测站的距离及其变化率作为冗余参量,将观测方程线性化.然后考虑方程中各项系数的误差及冗余参量与待估参量之间的函数关系,将定位问题建立为CTLS模型,并利用拉格朗日乘数法求解.最后推导了算法的克拉美罗界和理论误差.通过仿真实验证明了算法的有效性.     

基于区间全局优化的非线性最小二乘估计

分析了使用区间全局优化算法进行非线性系统模型参数估计的原因,介绍了非线性最小二乘估计和区间全局优化算法.在非线性系统模型参数估计中,相对于通过优化目标函数求得待估参数点估计的现有算法,基于区间分析的区间全局优化算法不仅可以求得待估参数的点估计,还可得到肯定包含待估参数真值的估计区间,并且该算法还具有计算结果稳定以及更大范围收敛的性质.通过仿真实验并与其他方法进行比较,结果表明算法的可行性和有效性.     

基于最小二乘法的雷达辐射源精确识别指标权重确定方法

雷达辐射源精确识别技术通过提取辐射源个体的固有指纹特征,可以在低信噪比、电磁信号密集的复杂电磁环境中对辐射源个体进行准确、有效识别,被广泛应用于电子情报侦察中.但目前的指纹特征赋权主要依靠专家经验,缺乏客观性.运用最小二乘法优化模型,对提取的指纹特征权值进行了分析计算,提高了辐射源精确识别指标权重确定的准确性、科学性及使用价值.     

一种基于拟牛顿法的递归最小二乘方法

基于拟牛顿优化方法,提出了一种稳健的自适应FIR滤波算法。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。通过计算机仿真比较了有关算法的性能。     

基于线性最小二乘方法的多基地声呐定位算法

通过对多基地声呐发射站和接收各站的测量结果进行数据融合,提出了基于线性最小二乘方法的多基地声呐空间定位算法,并针对影响算法定位精度性能的各个因素进行了分析.仿真结果显示:基于线性最小二乘方法的定位算法充分利用了系统的冗余信息,在全域范围内具有较高的定位精度,较现有单纯利用三观测量的目标三维定位算法的定位性能更为优越.该...