ARMA谱的自适应广义最小二乘估计法

本文在说明普通最小二乘自适应算法用于ARMA模型谱估计时存在有偏性的基础上,推广了最小二乘自适立算法的模型,得到了适用于在线ARMA谱估计的广义最小二乘自适应算法,并讨论了加速收敛的方法。与辅助变量(IV)法相比较,本文方法具有保证估计谱正性和能在线识别MA部分参数的优点。文中还用多种数值结果说明了本文方法在实际应用中的有效性。     

遗传优化支持向量机的传感器动态建模

传感器动态建模是研究传感器工作机理、评价动态性能指标及设计动态校正环节的重要途径.在分析最小二乘支持向量机回归算法的基础上,针对其可调参数的选择问题.提出了基于遗传算法进行全局优化的方法.仿真结果表明,将遗传优化最小二乘支持向量机算法应用于传感器的动态建模中,具有学习速度快和建模精度高的优点,所建模型具有较强的实用性和...     

最小二乘法在多传感器测量标定中的应用

在工程实际应用中建立起多变量测量的数学模型,为了给定数学模型中的系数参数,引入了最小二乘法的思想.根据最小二乘法的求解原理,建立了一种适用于多变量方程组系数参数的拟合算法.在多传感器测量标定和数据处理中加以应用,提高了计算精度.通过将其与一般算法进行比较,体现了最小二乘法的优越性.     

基于误差分布估计的机器人手眼标定方法研究

精确的机器人手眼标定对于机器人的视觉环境感知具有重要的意义。现有算法通常采用最小二乘估计或全局非线性优化求解方法对机器人手眼系统的变换参数进行估计。当系统存在测量粗差时直接采用最小二乘估计会导致标定结果精度的下降;基于全局非线性优化策略的标定算法则由于数据粗差的影响,求解过程易过早收敛也会造成标定精度低。为了解决误差粗差敏感的问题,提出了一种基于误差分布估计的加权最小二乘鲁棒估计方法,以提高机器人手眼标定的精度。首先,通过最小二乘估计计算手眼变换矩阵;之后计算每对坐标对应的误差值;根据误差值的分布概率初始化对应坐标数据的权值;最后采用加权的最小二乘估计重新计算机器人手眼标定矩阵。最后引入迭代估计策略进一步提高手眼标定的精度。设计的机器人手眼标定实验及结果证明,所提算法能够在数据粗差影响下保持较高的标定精度,更适用于机器人的手眼标定问题。     

基于MATLAB的递推最小二乘法辨识与仿真

通过对最小二乘算法的分析,推导出了递推最小二乘法的运算公式,提出了基于MATLAB/Simulink的使用递推最小二乘法进行参数辨识的设计与仿真方法。并采用Simulink建立系统的仿真对象模型和运用MATLAB的S-函数编写最小二乘递推算法,结合实例给出相应的仿真结果和分析。仿真结果表明,该仿真方法克服了传统编程语言仿真时繁杂、难度高、周期长的缺点,是一种简单、有效的最小二乘法的编程仿真方法。     

有理模型辨识的两类新方法-----混合迭代与柔性最小二乘法

针对有理模型提出两类辨识方法.首先提出基于递阶辨识思想的混合辨识方法,将模型分解为分子和分母两个子模型,分别用最小二乘法辨识分子参数,用粒子群算法和智能多步长梯度迭代算法辨识分母参数.由于降低了模型维数,且信息向量与噪声不相关,相对于传统的偏差补偿最小二乘算法,混合迭代法可以提高辨识精度并降低计算量.然后,为消除模型结构已知的假设,且充分利用最新数据更新系统参数,提出柔性递推最小二乘辨识方法,将有理模型转化为时变参数系统,进而辨识出时变系统的参数.仿真例子验证了所提出方法的有效性.     

满秩分解最小二乘法船舶航向模型辨识

为了解决标准遗忘因子最小二乘法在线辨识船舶航向模型参数漂移和发散问题,考虑到船舶在实际航行中存在海洋环境扰动和数据欠激励的情况,提出并验证了一种基于满秩分解的递推最小二乘法.用实船数据进行船舶航向模型参数辨识,将辨识结果与标准遗忘因子最小二乘算法、多新息最小二乘法、最小二乘支持向量机的辨识结果进行对比,验证了满秩分解有...     

基于LS–SVR的机器人空间4DOF无标定视觉定位

研究了基于智能算法的机器人无标定视觉伺服问题, 提出了一种新的基于最小二乘支持向量回归的机器人无标定视觉免疫控制方法. 利用最小二乘支持向量回归学习机器人位姿变化和观测到的图像特征变化之间的复杂非线性关系, 其中最小二乘支持向量回归的参数由自适应免疫算法加5折交叉检验优化确定, 在此基础上利用免疫控制原理设计了视觉控制器. 六自由度工业机器人空间4DOF 视觉定位实验结果表明了该方法的有效性.     

基于机器视觉的钢轨轮廓测量方法研究

钢轨轮廓检测是轨道几何参数检测的重点和难点。采用二维平面靶标对激光摄像传感器进行标定,建立世界坐标系和图像坐标系的标定模型。通过棋盘格进行实验标定,获取300组数据,采用非线性最小二乘法来标定模型参数。结果表明:采用非线性最小二乘法能够将棋盘格水平和垂直的测量误差控制在0.3 mm左右,该方法能够很好地运用到钢轨轮廓测量中。     

原油含水率自校准方法研究

原油含水率是评价油井产能的一项重要指标,但其易受矿化度、温度、流体状态、流体速度等参数的影响。针对基于电导法的原油含水率检测模型,通过室内外试验,得出了原油含水率的主要影响因素,建立了基于最小二乘法支持向量机的自校准模型。利用Matlab编写了自校准程序,运用10倍交叉验证的方法确定了最小二乘法支持向量机的优化参数,然后把达到预期拟合效果的自校准程序下载到DSP处理器中运行,实现了在一次仪表中对原油含水率测量结果的自校准。试验结果表明,原油含水率的影响因素与原油含水率之间呈非线性关系,自校准算法消除了主要干扰量对原油含水率的影响,实现了测量结果的自标定。和目前油田计量部门使用的蒸馏法相比,基于最小二乘法支持向量机的原油含水率检测模型不仅可以实现实时测量与校准,且原油含水率的相对误差比采用最小二乘法时有所减小。     

加权最小二乘联合遗传算法的无源定位

针对复杂环境下运动通信辐射源的无源定位,闭式解方法对于时频差模型中的测量噪声敏感且存在定位均方根误差较大问题.为了改善大观测误差下的定位性能,本文提出一种加权最小二乘联合遗传算法的递推式混合TDOA/FDOA定位方法.该方法首先利用已知站点观测大量时频差数据并建立误差模型,基于模型对定位过程中的多组时频差序列进行数据处理;其次通过加权最小二乘求解目标位置的初始值;然后采用改进的遗传算法在初始值的基础上通过多组时频差序列不断迭代、递推求解,修正位置坐标;最后利用位置估计和频差模型完成对目标速度估计.仿真结果表明,本文定位算法相比于经典两步加权最小二乘法具有更低的均方根误差,在大观测误差下能保持较高精度.同时相比于其他混合定位算法收敛速度快,可以有效减少计算量.     

Implementing a diffusion model optimized by a hybrid evolutionary algorithm to forecast notebook shipments

Taiwan computer firms need to forecast trends in notebook shipments. The Bass diffusion model has been successfully applied to describe the empirical adoption curve for many new products and technological innovations. In order to improve the parameter estimates, a hybrid evolutionary algorithm, which couples genetic algorithms (GAs) with particle swarm optimization (PSO), is proposed. This hybrid approach can produce more accurate estimates of the parameters for the Bass diffusion model. In addition, the price index plays an important role in the notebook market. Thus, the modified diffusion model is proposed to investigate the forecasting performance for notebook shipments. The results illustrate that a hybrid approach outperforms other methods such as nonlinear algorithm, GA and PSO in terms of mean absolute percentage error.     

Robust nonlinear model identification methods using forward regression

In this correspondence new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness via combined parameter regularization and new robust structural selective criteria. In parallel to parameter regularization, we use two classes of robust model selection criteria based on either experimental design criteria that optimizes model adequacy, or the predicted residual sums of squares (PRESS) statistic that optimizes model generalization capability, respectively. Three robust identification algorithms are introduced, i.e., combined A- and D-optimality with regularized orthogonal least squares algorithm, respectively; and combined PRESS statistic with regularized orthogonal least squares algorithm. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalization scheme in orthogonal least squares or regularized orthogonal least squares has been extended such that the new algorithms are computationally efficient. Numerical examples are included to demonstrate effectiveness of the algorithms.     

基于KRLS的非均匀采样非线性系统辨识

利用提升技术可将非均匀采样非线性系统离散化为一个多输入单输出传递函数模型,从而将系统输出表示为非均匀刷新非线性输入和输出回归项的线性参数模型,进一步基于非线性输入的估计或过参数化方法进行辨识.然而,当非线性环节结构未知或不能被可测非均匀输入参数化表示时,上述辨识方法将不再适用.为了解决这个问题,利用核方法将原始非线性数据投影到高维特征空间中使其线性可分,再对投影后的数据应用递推最小二乘算法进行辨识,提出基于核递推最小二乘的非均匀采样非线性系统辨识方法.此外,针对系统含有有色噪声干扰的情况,参考递推增广最小二乘算法的思想,利用估计残差代替不可测噪声,提出核递推增广最小二乘算法.最后,通过仿真例子验证所提算法的有效性.     

Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms

A novel hybrid learning algorithm based on a genetic algorithm to design a growing fuzzy neural network, named self-organizing fuzzy neural network based on genetic algorithms (SOFNNGA), to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. A new adding method based on geometric growing criterion and the epsiv-completeness of fuzzy rules is first used to generate the initial structure. Then a hybrid algorithm based on genetic algorithms, backpropagation, and recursive least squares estimation is used to adjust all parameters including the number of fuzzy rules. This has two steps: First, the linear parameter matrix is adjusted, and second, the centers and widths of all membership functions are modified. The GA is introduced to identify the least important neurons, i.e., the least important fuzzy rules. Simulations are presented to illustrate the performance of the proposed algorithm     

Adaptive recursive algorithm with logarithmic transformation for nonlinear system identification in α-stable noise

Although the least mean pth power (LMP) and normalized LMP (NLMP) algorithms of adaptive Volterra filters outperform the conventional least mean square (LMS) algorithm in the presence of α-stable noise, they still exhibit slow convergence and high steady-state kernel error in nonlinear system identification. To overcome these limitations, an enhanced recursive least mean pth power algorithm with logarithmic transformation (RLogLMP) is proposed in this paper. The proposed algorithm is adjusted to minimize the new cost function with the p-norm logarithmic transformation of the error signal. The logarithmic transformation, which can diminish the significance of outliers under α-stable noise environment, increases the robustness of the proposed algorithm and reduces the steady-state kernel error. Moreover, the proposed method improves the convergence rate by the enhanced recursive scheme. Finally, simulation results demonstrate that the proposed algorithm is superior to the LMP, NLMP, normalized least mean absolute deviation (NLMAD), recursive least squares (RLS) and nonlinear iteratively reweighted least squares (NIRLS) algorithms in terms of convergence rate and steady-state kernel error.     

Accelerated training algorithm for feedforward neural networks based on least squares method

A least squares based training algorithm for feedforward neural networks is presented. By decomposing each neuron of the network into a linear part and a nonlinear part, the learning error can then be minimized on each neuron by applying the least squares method to solve the linear part of the neuron. In all the problems investigated, the proposed algorithm is capable of achieving the required error level in one training iteration. Comparing to the conventional backpropagation algorithm and other fast training algorithms, the proposed training algorithm provides a major breakthrough in speeding up the training process.     

Identification and frequency domain analysis of non-stationary and nonlinear systems using time-varying NARMAX models

This paper introduces a new approach for nonlinear and non-stationary (time-varying) system identification based on time-varying nonlinear autoregressive moving average with exogenous variable (TV-NARMAX) models. The challenging model structure selection and parameter tracking problems are solved by combining a multiwavelet basis function expansion of the time-varying parameters with an orthogonal least squares algorithm. Numerical examples demonstrate that the proposed approach can track rapid time-varying effects in nonlinear systems more accurately than the standard recursive algorithms. Based on the identified time domain model, a new frequency domain analysis approach is introduced based on a time-varying generalised frequency response function (TV-GFRF) concept, which enables the analysis of nonlinear, non-stationary systems in the frequency domain. Features in the TV-GFRFs which depend on the TV-NARMAX model structure and time-varying parameters are investigated. It is shown that the high-dimensional frequency features can be visualised in a low-dimensional time–frequency space.     

Fast Blind Equalization Using Complex-Valued MLP

The blind equalizers based on complex valued feedforward neural networks, for linear and nonlinear communication channels, yield better performance as compared to linear equalizers. The learning algorithms are, generally, based on stochastic gradient descent, as they are simple to implement. However, these algorithms show a slow convergence rate. In the blind equalization problem, the unavailability of the desired output signal and the presence of nonlinear activation functions make the application of recursive least squares algorithm difficult. In this letter, a new scheme using recursive least squares algorithm is proposed for blind equalization. The learning of weights of the output layer is obtained by using a modified version of constant modulus algorithm cost function. For the learning of weights of hidden layer neuron space adaptation approach is used. The proposed scheme results in faster convergence of the equalizer.     

Adapting the search vector for direct adaptive control systems

In adaptive control algorithms, the adaptation routine (e.g., least squares or gradient) is usually used to adjust the controller parameters to approximate the ideal controller that is assumed to exist. Searching for the ideal parameter vector, a gradient-based hybrid adaptive routine is used here for continuous-time nonlinear systems. The adjustment of the parameter vector is usually based on minimizing the squared error. For direct adaptive control, in this paper an algorithm is presented to adapt the direction of the search vector so that the instantaneous control energy is minimized. Hence, the overall adaptive routine minimizes not only the squared error but also the instantaneous control energy. Stability results of the presented algorithm show that boundedness of the error is dependent on the length of the search vector.