Separating iterative solution model of generalized nonlinear dynamic least squares for data processing in building of digital earth

Data coming from different sources have different types and temporal states. Relations between one type of data and another ones, or between data and unknown parameters are almost nonlinear. It is not accurate and reliable to process the data in building the digital earth with the classical least squares method or the method of the common nonlinear least squares. So a generalized nonlinear dynamic least squares method was put forward to process data in building the digital earth. A separating solution model and the iterative calculation method were used to solve the generalized nonlinear dynamic least squares problem. In fact, a complex problem can be separated and then solved by converting to two sub problems, each of which has a single variable. Therefore the dimension of unknown parameters can be reduced to its half, which simplifies the original high dimensional equations.

Solving method of generalized nonlinear dynamic least squares for data processing in building of digital mine

Data are very important to build the digital mine. Data come from many sources, have different types and temporal states. Relations between one class of data and the other one, or between data and unknown parameters are more nonlinear. The unknown parameters are non random or random, among which the random parameters often dynamically vary with time. Therefore it is not accurate and reliable to process the data in building the digital mine with the classical least squares method or the method of the common nonlinear least squares. So a generalized nonlinear dynamic least squares method to process data in building the digital mine is put forward. In the meantime, the corresponding mathematical model is also given. The generalized nonlinear least squares problem is more complex than the common nonlinear least squares problem and its solution is more difficultly obtained because the dimensions of data and parameters in the former are bigger. So a new solution model and the method are put forward to solve the generalized nonlinear dynamic least squares problem. In fact, the problem can be converted to two sub problems, each of which has a single variable. That is to say, a complex problem can be separated and then solved. So the dimension of unknown parameters can be reduced to its half, which simplifies the original high dimensional equations. The method lessens the calculating load and opens up a new way to process the data in building the digital mine, which have more sources, different types and more temporal states.

NLS次优简化算法及其在单站无源定位中的应用

为了简化基于外辐射源固定单站无源定位求解算法以用于工程实现,建立了定位原理极坐标模型,给出了极坐标系下非线性最小二乘(NLS)定位算法,根据极坐标系下定位算法的特点,为简化计算,提出了NLS次优简化算法。仿真结果表明,NLS次优简化算法有较好的稳定度和定位精度,为工程实现提供了参考。

基于NLS-RELAX的空中多机动目标参数估计

针对机载雷达在环境复杂的高空飞行时检测空中多机动目标难度大的问题,提出了一种基于NLS-RELAX的空中多机动目标参数估计方法。本方法借鉴了RELAX算法的思想,可对多个机动目标进行有效分离,并利用非线性最小二乘(Nonlinear Least Squares,NLS)对分离后的每个机动目标信号进行参数估计。仿真实验表明,在目标信号严重被强杂波淹没且脉冲数有限的情况下,本方法可以获得较为精确的参数估计结果,且由于加入了RELAX的分离思想,在待检测距离单元内存在多个机动目标时本方法同样适用。

Nonlinear Modeling and Identification of the Electro-hydraulic Control System of an Excavator Arm Using BONL Model

Electro-hydraulic control systems are nonlinear in nature and their mathematic models have unknown parameters. Existing research of modeling and identification of the electro-hydraulic control system is mainly based on theoretical state space model, and the parameters identification is hard due to its demand on internal states measurement. Moreover, there are also some hard-to-model nonlinearities in theoretical model, which needs to be overcome. Modeling and identification of the electro-hydraulic control system of an excavator arm based on block-oriented nonlinear（BONL） models is investigated. The nonlinear state space model of the system is built first, and field tests are carried out to reveal the nonlinear characteristics of the system. Based on the physic insight into the system, three BONL models are adopted to describe the highly nonlinear system. The Hammerstein model is composed of a two-segment polynomial nonlinearity followed by a linear dynamic subsystem. The Hammerstein-Wiener（H-W） model is represented by the Hammerstein model in cascade with another single polynomial nonlinearity. A novel Pseudo-Hammerstein-Wiener（P-H-W） model is developed by replacing the single polynomial of the H-W model by a non-smooth backlash function. The key term separation principle is applied to simplify the BONL models into linear-in-parameters struc^tres. Then, a modified recursive least square algorithm（MRLSA） with iterative estimation of internal variables is developed to identify the all the parameters simultaneously. The identification results demonstrate that the BONL models with two-segment polynomial nonlinearities are able to capture the system behavior, and the P-H-W model has the best prediction accuracy. Comparison experiments show that the velocity prediction error of the P-H-W model is reduced by 14%, 30% and 75% to the H-W model, Hammerstein model, and extended auto-regressive （ARX） model, respectively. This research is helpful in controller design, system monitoring and diagnosis.

Nondependent-derivative method to process nonlinear data in digital science engineering

Data, including the spatial data and the non-spatial data, are the basis of all digital scientific engineering projects, such as the digital earth and the digital nation, the digital mine. The spatial data have the characteristics of many sources, multi-dimension, multi-type, many time states and different accuracy. The spatial data firstly must be processed before using these data. The parameter estimation model to process the data is commonly the more complex nonlinear model including random parameters and non-random parameters. So a generalized nonlinear dynamic least squares method to process these data is put forward. According to the special structure of the generalized nonlinear dynamic least squares problem and the solution to the first order, a new solving model and a corresponding method to process the problem are put forward. The complex problem can be divided into two sub-problems so that the number of the unknown parameters is reduced largely. Therefore it reduces the computing difficulty and load.

LS-SVM model based nonlinear predictive control for MCFC system

This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be controlled efficiently. First, the output voltage of an MCFC stack is identified by a least squares support vector machine (LS-SVM) method with radial basis function (RBF) kernel so as to implement nonlinear predictive control. And then, the optimal control sequences are obtained by applying genetic algorithm (GA). The model and controller have been realized in the MATLAB environment. Simulation results indicated that the proposed controller exhibits satisfying control effect.

基于混合威布尔分布的水稻插秧机的可靠性分析及剩余寿命预测

为了更准确描述水稻插秧机的失效规律,提高可靠性分析的准确性,对水稻插秧机的故障数据进行分析,采用两参数混合威布尔分布对水稻插秧机进行建模。以残差平方和最小为优化目标,建立参数估计优化模型,利用改进粒子群算法对其进行求解,然后采用K-S检验法对模型进行检验,对比单一威布尔模型、混合威布尔模型与水稻插秧机失效数据之间的拟合程度,得出使用两参数混合威布尔模型评估水稻插秧机可靠性的合理性,在此模型的基础上计算得到水稻插秧机的平均无故障工作时间为161.75 h,中位寿命为147.14 h,特征寿命为191.31 h,且在可靠度为0.6时,预防性维修周期为115.19 h,最后在混合威布尔分布模型的基础上计算出剩余寿命-可靠度的关系,可定量分析插秧机在一定使用时间下的剩余寿命,从而进行预测性维护。

Study on Coal Consumption Curve Fitting of the Thermal Power Based on Genetic Algorithm

Coal consumption curve of the thermal power plant can reflect the function relationship between the coal consumption of unit and load, which plays a key role for research on unit economic operation and load optimal dispatch. Now get coal consumption curve is generally obtained by least square method, but which are static curve and these curves remain unchanged for a long time, and make them are incompatible with the actual operation situation of the unit. Furthermore, coal consumption has the characteristics of typical nonlinear and time varying, sometimes the least square method does not work for nonlinear complex problems. For these problems, a method of coal consumption curve fitting of the thermal power plant units based on genetic algorithm is proposed. The residual analysis method is used for data detection;quadratic function is employed to the objective function;appropriate parameters such as initial population size, crossover rate and mutation rate are set;the unit’s actual coal consumption curves are fitted, and comparing the proposed method with least squares method, the results indicate that fitting effect of the former is better than the latter, and further indicate that the proposed method to do curve fitting can best approximate known data in a certain significance, and they can real-timely reflect the interdependence between power output and coal consumption.

Modified Levenberg-Marquardt algorithm for source localization using AOAs in the presence of sensor location errors

In this paper,by utilizing the angle of arrivals(AOAs) and imprecise positions of the sensors,a novel modified Levenberg-Marquardt algorithm to solve the source localization problem is proposed.Conventional source localization algorithms,like Gauss-Newton algorithm and Conjugate gradient algorithm are subjected to the problems of local minima and good initial guess.This paper presents a new optimization technique to find the descent directions to avoid divergence,and a trust region method is introduced to accelerate the convergence rate.Compared with conventional methods,the new algorithm offers increased stability and is more robust,allowing for stronger non-linearity and wider convergence field to be identified.Simulation results demonstrate that the proposed algorithm improves the typical methods in both speed and robustness,and is able to avoid local minima.

LEAST-SQUARES MIXED FINITE ELEMENT METHODS FOR NONLINEAR PARABOLIC PROBLEMS

Focuses on the formulation of a number of least-squares mixed finite element schemes to solve the initial boundary value problem of a nonlinear parabolic partial differential equation. Convergence analysis; Information on the least-squares mixed element schemes for nonlinear parabolic problem.

正弦余弦算法求解含有异常值的非线性数据拟合

针对非线性数据拟合问题,建立以残差的平方和与绝对值和为目标的最小二乘与最小一乘模型,采用正弦余弦算法计算模型参数.计算结果表明:如果数据的分布是对称且无异常值,则最小二乘得到的结果与最小一乘得到的结果基本一致;如果数据存在异常值,则异常值对最小二乘有着较大的影响,而对最小一乘的影响较小.

基于部分解耦合的非线性数字干扰对消算法

针对数字对消器中基于并行Hammerstein(parallel Hammerstein,PH)模型的非线性最小均方(least mean square,LMS)算法基函数之间的强相关性而存在收敛速度变慢、收敛稳定性变差、稳态误差增大的问题,提出了一种采用部分解耦合策略的改进算法。通过引入带约束的正则方程,实现各个非线性阶次独立的迭代步长设定。并且对基于部分解耦合干扰对消算法进行解析分析,给出了平均和均方意义下的收敛条件。仿真结果表明,所提方法在几乎不损失稳态误差性能的前提下,提升了非线性LMS算法收敛速度,改善了收敛性能,并且针对滤波器非线性阶数和记忆长度变化具有良好的鲁棒性。在1 MHz带宽二进制相移键控(binary phase shift keying,BPSK)干扰和加性高斯白噪声情况下干噪比设置为20 dB,相较于传统非线性LMS算法,所提算法可以实现最大0.5倍迭代步长上界、最高非线性阶次9以及最大记忆长度12的设定。

基于LS-SVM的宽带接收前端非线性补偿算法

针对目前常用的基于参数化非线性模型(Parameterized Nonlinear Model,PNM)的补偿算法存在易陷入局部最小值,导致补偿性能不稳的问题,该文提出了基于最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的宽带接收前端非线性补偿算法.该算法基于减谱-时频变换法(Spectrum Reduction Algorithm based on Time-Frequency Conversion,SRA-TFC)盲分离接收前端输出信号中的大功率基波信号和其他小功率信号,并以此作为LS-SVM逆模型的训练输入-输出样本对.引入最小二乘支持向量回归(Least Squares Support Vector Regression,LS-SVR)算法高精度拟合接收前端非线性逆模型.通过以宽带接收前端的输出信号为测试样本消除其非线性失真分量.仿真与实测结果表明:该算法可使宽带接收前端的无杂散失真动态范围(Spurs-Free-Dynamic-Range,SFDR)提高约20 dB,较基于PNM的补偿算法提高了约5 dB.

Squint-mode InISAR imaging based on nonlinear least square and coordinates transform

Aiming at the interferometric inverse synthetic aperture radar(InISAR) imaging in the presence of squint,we investigate the influence of squint on the InISAR imaging. First,coupling of the squint additive phase and the target azimuth/altitude coordinates to be solved may make the solution more difficult. Second,the squint angle may lead to estimation error of the vertical coordinates and distortion of the ultimate image. Traditional InISAR imaging algorithms can not solve the above two problems effectively,so we propose a new method which combines the nonlinear least square(NLS) and coordinates transform(CT) to estimate the target coordinates,and a three-dimensional(3-D) image consistent with the real target is obtained accordingly. Simulations show that the proposed method is effective for the squint-mode InISAR imaging.

Damage Identification under Incomplete Mode Shape Data Using Optimization Technique Based on Generalized Flexibility Matrix

A generalized flexibility–based objective function utilized for structure damage identification is constructed for solving the constrained nonlinear least squares optimized problem. To begin with, the generalized flexibility matrix (GFM) proposed to solve the damage identification problem is recalled and a modal expansion method is introduced. Next, the objective function for iterative optimization process based on the GFM is formulated, and the Trust-Region algorithm is utilized to obtain the solution of the optimization problem for multiple damage cases. And then for computing the objective function gradient, the sensitivity analysis regarding design variables is derived. In addition, due to the spatial incompleteness, the influence of stiffness reduction and incomplete modal measurement data is discussed by means of two numerical examples with several damage cases. Finally, based on the computational results, it is evident that the presented approach provides good validity and reliability for the large and complicated engineering structures.

在线鲁棒最小二乘支持向量机回归建模

鉴于工业过程的时变特性以及现场采集的数据通常具有非线性特性且包含离群点,利用最小二乘支持向量机回归(least squares support vector regression,LSSVR)建模易受离群点的影响.针对这一问题,结合鲁棒学习算法(robust learning algorithm,RLA),本文提出了一种在线鲁棒最小二乘支持向量机回归建模方法.该方法首先利用LSSVR模型对过程输出进行预测,与真实输出相比较得到预测误差;然后利用RLA方法训练LSSVR模型的权值,建立鲁棒LSSVR模型;最后应用增量学习方法在线更新鲁棒LSSVR模型,从而得到在线鲁棒LSSVR模型.仿真研究验证了所提方法的有效性.

基于非线性最小二乘的三星座时差定位综合算法

提出一种基于非线性最小二乘(NLS)的三星座时差定位综合算法。该算法不使用WGS-84椭球模型作为定位约束条件,通过对卫星过顶前后的两组弱观测数据进行综合、配对处理,实现对目标的精确定位。仿真结果表明,该方法不仅对弱观测数据具有较好的定位精度,而且不受目标高程约束,当目标高程不确定时,仍具有稳健的定位效果。

高压XLPE电缆绝缘多参数在线监测

建立了在交流电压作用下高压交联聚乙烯(XLPE)电缆绝缘的参数模型,并分析了电缆绝缘劣化时绝缘参数的外在表现。提出通过监测XLPE电缆的工作绝缘电阻、等值电容、介质损耗因数及各参数的变化趋势来综合评定电缆的绝缘状况,并分析了该方法的有效性及合理性。设计了高压XLPE电缆在线监测装置。在数据处理方面,采用基于LM(Levenberg-Marquardt)算法的非线性最小二乘拟合法对采样信号进行拟合,将信号进行快速傅里叶变换得到的谐波分析结果作为LM算法的迭代初值,并利用拟合结果计算出电缆绝缘的被监测参数。仿真分析表明,该算法收敛速度快、精度高,能很好地抑制电网频率波动、直流分量以及谐波含量对被监测参数的影响。

自适应光学系统变形镜控制电压预测

在校正大气湍流畸变波前相差的自适应光学系统中,利用基于Levenberg-Marquardt学习算法的非线性反向传播神经网络技术(LMBP)对变形镜控制电压进行预测。以对受横向风影响的大气湍流畸变波前斜率数据为研究对象,通过数值仿真方法,研究了基于LMBP算法的自适应光学系统变形镜电压非线性预测控制算法。通过实验发现,预测电压和变形镜实际控制电压拟合效果良好。讨论了回溯帧数对预测效果的影响,并与基于递推最小二乘(RLS)算法的线性预测算法进行比较。对比结果表明,基于LMBP算法的非线性电压预测方法比基于递推最小二乘法的线性电压预测方法能更有效地降低系统由伺服延迟引起的误差。