Sparse LMS/F algorithms with application to adaptive system identification

Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advantages of both least mean square (LMS) and least mean fourth (LMF). The advantage of LMS is fast convergence speed while its shortcoming is suboptimal solution in low signal‐to‐noise ratio (SNR) environment. On the contrary, the advantage of LMF algorithm is robust in low SNR while its drawback is slow convergence speed in high SNR case. Many finite impulse response systems are modeled as sparse rather than traditionally dense. To take advantage of system sparsity, different sparse LMS algorithms with l_p‐LMS and l₀‐LMS have been proposed to improve adaptive identification performance. However, sparse LMS algorithms have the same drawback as standard LMS. Different from LMS filter, standard LMS/F filter can achieve better performance. Hence, the aim of this paper is to introduce sparse penalties to the LMS/F algorithm so that it can further improve identification performance. We propose two sparse LMS/F algorithms using two sparse constraints to improve adaptive identification performance. Two experiments are performed to show the effectiveness of the proposed algorithms by computer simulation. In the first experiment, the number of nonzero coefficients is changing, and the proposed algorithms can achieve better mean square deviation performance than sparse LMS algorithms. In the second experiment, the number of nonzero coefficient is fixed, and mean square deviation performance of sparse LMS/F algorithms is still better than that of sparse LMS algorithms. Copyright © 2013 John Wiley & Sons, Ltd.     

抗差Vondrak滹波器设计及其在粗差探测中的应用

首先,分析了Vondrak滤波器设计的基本原理,在原有模型的基础上引入一个惩罚函数,利用惩罚函数来抑制粗差对拟合的影响,得到抗差Vondrak滤波模型;然后,给出了抗差Von-drak滤波模型的迭代求解算法,利用抗差Vondrak滤波器获得原始信号在粗差污染情况下的稳健估计,进而利用估计残差的大小进行粗差探测;最后,分别利用仿真数据和实测数据进行粗差探测试验,试验结果验证了抗差Vondrak滤波的正确性和有效性。     

纸浆洗涤过程中稀释因子的优化

基于浆料洗涤系统的整体工程思想,对洗浆系统和蒸发系统进行了物料衡算,建立了纸浆洗涤用水和稀释因子的最优化模型,并通过罚函数,将约束问题转化为无约束问题,对模型进行了寻优控制.实践表明,该方法可以找到最优稀释因子,减少了费用,降低了生产成本.     

基于鱼体回声图像的鱼类识别算法

以图像处理技术理论为前提,使用Matlab中的DIP(Digtal Image Process)工具箱和GUI(Graph User Interface)工具,对鱼体回声图像进行处理,匹配专家数据库,成功实现了对单体鱼的识别.介绍了对单体鱼的识别方法,详细阐述了鱼体回声图像的预处理方法、单体鱼的体态及长度参数的提取方法、通过建立专家数据库来实现目标识别的方法.引入了自适应图像窗的概念,提出了对鱼群的识别算法.     

节能减排背景下的机组组合模型和算法研究

在节能减排背景下对机组组合目标函数进行了调整,采用排放价格因子将系统总能耗最小和总排放最小的多目标问题转化为单目标问题,利于全网开展机组组合和经济调度.将多时段的机组组合问题在时间上解耦,转化为单小时的子问题,并采用基于内点法的混合整数规划法求解子问题.通过两个算例验证了算法和模型的有效性.     

Airfoil optimization for wind turbine application

An airfoil optimization method for wind turbine applications that controls the loss in performance due to leading edge contamination is developed and tested. The method uses the class‐shape‐transformation technique to parametrize the airfoil geometry and uses an adjusted version of the panel code XFOIL to calculate the aerodynamic performance. To find optimal airfoil shapes, the derivative‐free Covariance Matrix Adaptation Evolution Strategy is used in combination with an adaptive penalty function. The method is tested for the design of airfoils for the outer part of a megawatt‐class wind turbine rotor blade, and the results are compared with airfoils from Delft University. It is found that the method is able to automatically create airfoils with equal or improved performance compared with the Delft designs. For the tested application, the adjustments performed to the XFOIL code improve the maximum lift, post stall, and the overall drag predictions.     

Using a spectral scaling structured BFGS method for constrained nonlinear least squares

In this paper, we use a spectral scaled structured BFGS formula for approximating projected Hessian matrices in an exact penalty approach for solving constrained nonlinear least-squares problems. We show this spectral scaling formula has a good self-correcting property. The reported numerical results show that the use of the spectral scaling structured BFGS method outperforms the standard structured BFGS method.     

三维恒定磁场罚函数有限元法的离散格式

对于三雏线性恒定磁场的罚画数有限元法，本文给出了关于四面体元、直三棱柱元和长方体元这三种常用单元的离散格式。     

惩罚函数结合遗传算法的PID参数优化

针对传统优化算法对PID参数优化时,由于目标函数选择不当出现的超调量过大,从而引起系统的反应速度变慢和稳定性变差的问题,提出以传统遗传算法为基础,引入惩罚函数的方法,根据系统中出现的超调量来构造新的目标函数实现对PID的参数优化。实验表明,该方法可以有效减少系统中的超调量,加快优化以及运行速度的同时保证系统的稳定性,实现对PID参数的优化。     

基于惩罚项的热连轧轧制规程多目标函数优化

为了解决传统热连轧负荷分配优化普遍采用约束方法进行求解时求解步骤繁琐、不容易获得最优解的问题,提出了一种惩罚函数算法.通过引入函数惩罚项,将多目标函数约束求解问题转化为无约束求解问题.建立了带有惩罚项的轧制力、板形、功率和温度的单目标函数,进而采用线性加权求和法建立了综合多目标函数.利用Nelder和Mead单纯形解法则进行求解,简化了求解的步骤,最终得到了兼顾轧制力平衡、板形最优及温度合理的负荷分配方案.实际应用效果表明,与传统负荷分配方式相比,基于多目标优化得到的轧制规程更符合实际生产要求,具有良好的应用前景.