抛物型方程的演化参数识别方法

给出了一种利用演化计算方法求解微分方程中的参数识别类型反问题的方法。该方法把参数识别问题转化为泛函的优化问题用演化算法来求解,指定待定参数的函数类形式,用遗传算法(Genetic Algorithms)来演化待求参数的最优估计值,并将该方法运用于线性扩散方程和拟线性对流扩散反方程反问题的数值模拟中。     

用于电介质中空间电荷分布测量的Tikhonov反卷积算法

研究了使用压力波法测量平板电介质试样的空间电荷分布的数值解法,使用基于Tikhonov正则化方法的反卷积算法得到了真实的空间电荷分布.在反卷积算法中使用了相关的技术处理,如小波包过滤高频噪音,Tikhonov正则化方法处理积分方程等.利用数值实验研究了噪声对反卷积算法的影响,结果表明,在无噪或者低噪环境下,反卷积算法能够非常好地计算出电介质中的空间电荷分布;在处理有噪数据时,反卷积的结果受到明显的影响,但仍然有较高的计算精度.正则化参数α对空间电荷分布的数值解起着明显的光滑作用,但是对于解的积分值却影响不大.对实际测量数据进行处理的结果表明,反卷积算法成功地计算出了固体电介质中的空间电荷分布和电场分布.     

块稀疏水声信道的改进压缩感知估计

压缩感知信道估计可利用信道稀疏特性提高估计性能,但对于具有典型块稀疏分布的水声信道,经典的l₀或l₁范数无法很好地描述块稀疏特性。利用水声信道块稀疏分布规律特性提出一种能够识别块稀疏结构的块稀疏似零范数,并在稀疏恢复信道估计算法中引入块稀疏似零范数约束项,进一步推导了复数域块稀疏似零范数恢复迭代算法,该算法通过对块稀疏似零范数进行梯度下降迭代并将梯度解投影至解空间来获得水声信道的块稀疏似零范数估计。数值仿真和海上水声通信实验结果表明该算法相对经典的稀疏信道估计算法有较明显的性能改善。通过算法推导、仿真和实验可获取结论:利用水声信道的块稀疏特性进行压缩感知重构可有效提高信道估计性能。     

Reconstruction of the equilibrium of the plasma in a Tokamak and identification of the current density profile in real time

The reconstruction of the equilibrium of a plasma in a Tokamak is a free boundary problem described by the Grad–Shafranov equation in axisymmetric configuration. The right-hand side of this equation is a nonlinear source, which represents the toroidal component of the plasma current density. This paper deals with the identification of this nonlinearity source from experimental measurements in real time. The proposed method is based on a fixed point algorithm, a finite element resolution, a reduced basis method and a least-square optimization formulation. This is implemented in a software called Equinox with which several numerical experiments are conducted to explore the identification problem. It is shown that the identification of the profile of the averaged current density and of the safety factor as a function of the poloidal flux is very robust.     

Non-linear parameter estimation with Volterra series using the method of recursive iteration through harmonic probing

Volterra series provides a platform for non-linear response representation and definition of higher order frequency response functions (FRFs). It has been extensively used in non-parametric system identification through measurement of first and higher order FRFs. A parametric system identification approach has been adopted in the present study. The series response structure is explored for parameter estimation of polynomial form non-linearity. First and higher order frequency response functions are extracted from the measured response harmonic amplitudes through recursive iteration. Relationships between higher order FRFs and first order FRF are then employed to estimate the non-linear parameters. Excitation levels are selected for minimum series approximation error and the number of terms in the series is controlled according to convergence requirement. The problem of low signal strength of higher harmonics is investigated and a measurability criterion is proposed for selection of excitation level and range of excitation frequency. The procedure is illustrated through numerical simulation for a Duffing oscillator. Robustness of the estimation procedure in the presence of measurement noise is also investigated.     

Target detection for low cost uncooled MWIR cameras based on empirical mode decomposition

In this work, a novel method for detecting low intensity fast moving objects with low cost Medium Wavelength Infrared (MWIR) cameras is proposed. The method is based on background subtraction in a video sequence obtained with a low density Focal Plane Array (FPA) of the newly available uncooled lead selenide (PbSe) detectors. Thermal instability along with the lack of specific electronics and mechanical devices for canceling the effect of distortion make background image identification very difficult. As a result, the identification of targets is performed in low signal to noise ratio (SNR) conditions, which may considerably restrict the sensitivity of the detection algorithm. These problems are addressed in this work by means of a new technique based on the empirical mode decomposition, which accomplishes drift estimation and target detection. Given that background estimation is the most important stage for detecting, a previous denoising step enabling a better drift estimation is designed. Comparisons are conducted against a denoising technique based on the wavelet transform and also with traditional drift estimation methods such as Kalman filtering and running average. The results reported by the simulations show that the proposed scheme has superior performance.     

基于混沌蚂蚁群算法的Lorenz混沌系统的参数估计

通过构造一个适当的适应度函数，首先将混沌系统的参数估计问题转化为参数的寻优问题，之后利用混沌蚂蚁群算法的全局优化搜索能力对这个问题进行求解.以典型的Lorenz混沌系统为例进行了数值模拟.实验数值仿真结果表明，使用该方法可以对混沌系统的未知参数进行有效地估计.     

基于量子粒子群算法的混沌系统参数辨识

针对混沌系统参数辨识问题, 在基本群智能算法粒子群优化算法的基础上, 提出量子粒子群算法, 测试函数证明了算法具有良好的全局优化能力. 进而将其应用于混沌系统参数辨识问题, 将参数辨识问题转化为多维函数空间上的优化问题. 通过对平衡板热对流典型混沌系统Lorenz系统进行研究, 并与基本算法和遗传算法比较. 仿真实验证明, 算法的有效性, 对混沌理论的发展有着非常重要的意义. 关键词： 量子粒子群算法 混沌系统 系统辨识     

Palmprint recognition based on the representation in the feature space

In this paper, we propose a palmprint recognition method based on the representation in the feature space. The proposed method seeks to represent the test sample as a linear combination of all the training samples in the feature space and then exploits the obtained linear combination to perform palmprint recognition. We can implement the mapping from the original space to the feature space by using the kernel functions such as radial basis function (RBF). In this method, the selection of the parameter of the kernel function is important. We propose an automatic algorithm for selecting the parameter. The basic idea of the algorithm is to optimize the feature space such that the samples from the same class are well clustered while the samples from different classes are pushed far away. The proposed criterion measures the goodness of a feature space, and the optimal kernel parameter is obtained by minimizing this criterion. Experimental results on multispectral palmprint database show that the proposed method is more effective than 2DPCA, 2DLDA, AANNC, CRC_RLS, nearest neighbor method (NN) and competitive coding method in terms of the correct recognition rate.     

一种基于混沌系统部分序列参数辨识的混沌保密通信方法

本文提出一种混沌保密通信方法,即混沌系统的部分序列用于混沌系统参数辨识其他序列用于通信保密.利用混沌蚁群优化算法对部分序列混沌系统进行参数辨识,以达到了解混沌系统全部信息的目的.在参数辨识过程中引入参数空间和蚁群空间,通过空间变换函数使参数空间与蚁群空间之间相互变换.文中使用Lorenz系统进行数值试验,其结果验证混沌系统部分序列参数辨识及混沌保密通信的可行性.     

基于最大似然的单通道交叠激光微多普勒信号参数分离估计

利用激光对目标微弱振动进行探测有利于获得明显的微多普勒效应,这为精确估计目标微动特征参数,实现对目标的分类和精细识别提供了可能.但对于多散射点或多目标激光探测,信号为单通道多分量微动混合的形式,而且补偿目标主体运动后,数值上相近的微动参数还会导致信号在时频域存在严重的交叠.为从这类混合信号中精确估计各分量的微动参数,本文提出了基于最大似然框架的参数分离估计方法.利用精细化扫描的奇异值比谱法从混合信号中获得目标微动频率,并得到各分量的幅值比信息.推导了微动参数最大似然估计的解析表达形式,根据激光微多普勒信号的特点从频谱能量分布的角度重新设计了似然函数,解决了传统似然函数在激光微动信号中出现的高度非线性问题,降低了初始化的要求,提高了抗噪性能,并采用马尔可夫-蒙特卡罗方法具体实现了参数的估计.在微动参数得到估计的基础上给出了信号的幅值和初相的估计方法.用本文方法对仿真和实验数据进行处理,得到了接近克拉美罗下界的估计结果,验证了方法的有效性.与传统非参数化估计方法的对比结果体现了所提方法对混合微动参数精确估计上的优势.     

A General Metric for the Similarity of Both Stochastic and Deterministic System Dynamics

Many problems in the study of dynamical systems—including identification of effective order, detection of nonlinearity or chaos, and change detection—can be reframed in terms of assessing the similarity between dynamical systems or between a given dynamical system and a reference. We introduce a general metric of dynamical similarity that is well posed for both stochastic and deterministic systems and is informative of the aforementioned dynamical features even when only partial information about the system is available. We describe methods for estimating this metric in a range of scenarios that differ in respect to contol over the systems under study, the deterministic or stochastic nature of the underlying dynamics, and whether or not a fully informative set of variables is available. Through numerical simulation, we demonstrate the sensitivity of the proposed metric to a range of dynamical properties, its utility in mapping the dynamical properties of parameter space for a given model, and its power for detecting structural changes through time series data.     

运动目标激光微多普勒效应平动补偿和微动参数估计

利用激光探测微多普勒效应可以精确估计微动参数,有利于实现目标的准确分类和精细识别.运动目标的微多普勒效应是一种由多项式相位信号模型与正弦调频模型组成的混合信号.对于这类混合信号中的微动参数估计目前还未提出有效的方法.对此,本文提出一种基于分数阶傅里叶变换(Fr FT)的平动补偿方法,通过设计对Fr FT参数域的带宽搜索方法,可以从混合信号中精确估计平动参数,实现平动和微动的分离;通过设计静态参数粒子滤波器,从补偿后的信号中准确估计了微动参数;针对静态参数模型,采用马尔可夫-蒙特卡罗方法增加粒子多样性,并利用累积残差定义新的粒子权重计算函数,保证了算法在对多维参数估计时的快速有效收敛,避免了参数分别估计时误差传递的影响.通过仿真分析对比和实验数据,验证了本文所提补偿和参数估计算法的有效性.     

基于Huber的高阶容积卡尔曼跟踪算法

为改善高阶容积卡尔曼滤波算法的滤波精度和鲁棒性, 提出了一种新的基于Huber的高阶容积卡尔曼滤波算法. 在采用统计线性回归模型近似非线性量测模型的基础上, 利用Huber M 估计算法实现状态的量测更新. 进一步结合高阶球面-径向容积准则的状态预测模块构成基于 Huber的高阶容积卡尔曼跟踪算法. 重点分析了Huber代价函数的调节因子对算法跟踪性能的影响. 通过对纯方位目标跟踪和再入飞行器跟踪两个实例验证了所提算法的跟踪性能优于传统高阶容积卡尔曼滤波算法.     

一种时间序列的弱非线性检验方法

时间序列的非线性是判定该时间序列具有混沌特性的必要条件.提出一种基于线性和非线性AR模型归一化多步预测误差比值的非线性检验量δ_NAR,采用替代数据法来检测时间序列中的弱非线性.以Lorenz时间序列为例,分析了估计非线性检验量δ_NAR时各相关参数对弱非线性检测性能的影响.通过混沌时间序列非线性检测试验,对4种混沌时间序列中的3种,非线性检验量δ_NAR都表现出比基于AIC模型选择准则的非线性检验量相似文献   

基于L型延迟阵列调制宽带转换器的信号载频和二维到达角联合估计

针对现有的基于欠采样的频率和二维到达角的联合估计存在结构复杂问题,本文提出了一种基于调制宽带转换器技术的L型延迟阵列接收结构.利用延迟通道与未延迟通道采样值之间的相位差可直接估计载频,进而计算二维到达角,无需额外的参数配对操作,避免了配对步骤引入的误差和复杂度的提升.并结合所提L型延迟阵列结构的特点构造相关矩阵和三线性模型,提出了两种参数估计算法,一种基于旋转不变子空间算法,计算量小,适用于需要实时处理的场景;另一种基于正则分解技术,鲁棒性较好,适用于信噪比较低的应用场景.仿真实验表明该方法能较好地从欠奈奎斯特样本中估计目标的载频和二维到达角参数.     

A numerical algorithm for the solution of a phase-field model of polycrystalline materials

We describe an algorithm for the numerical solution of a phase-field model (PFM) of microstructure evolution in polycrystalline materials. The PFM system of equations includes a local order parameter, a quaternion representation of local orientation and a species composition parameter. The algorithm is based on the implicit integration of a semidiscretization of the PFM system using a backward difference formula (BDF) temporal discretization combined with a Newton–Krylov algorithm to solve the nonlinear system at each time step. The BDF algorithm is combined with a coordinate-projection method to maintain quaternion unit length, which is related to an important solution invariant. A key element of the Newton–Krylov algorithm is the selection of a preconditioner to accelerate the convergence of the Generalized Minimum Residual algorithm used to solve the Jacobian linear system in each Newton step. Results are presented for the application of the algorithm to 2D and 3D examples.     

A Generalized Estimate of the SLRBPolynomial Ripples for RF Pulse Generation

The nonlinearity of the parameter relations for the Shinnar–Le Roux RF pulse design algorithm has induced to performa classification based on the features of the slice profile dueto the RF pulse. In the present paper a generalization ofthe relation between the ripple amplitudes of the SLRBpolynomial and those of the slice profile is given. It allows generation of RF pulses with better slice profiles and slightly reduced energy, avoiding anya prioriclassification. The effect of our estimation has been shown by generating several pulses by generalized estimation ofBpolynomial ripples. In addition, their behavior has been compared to that of analogous pulses generated by means of the classification just mentioned.     

STRATEGY FOR FINDING A NEAR-OPTIMAL MEASUREMENT SET FOR PARAMETER ESTIMATION FROM MODAL RESPONSE

Algorithms that estimate structural parameters from modal response using least-squares minimization of force or displacement residuals generally do not have unique solutions when the data are spatially sparse. The number and character of the multiple solutions depend upon the physical features of the structure and the locations of the response measurements. It has been observed that both the number of solutions and the sensitivity of the parameter estimates to measurement noise is greatly influenced by the choice of measurement locations. In this paper, we present a heuristic method to select a near-optimal subset of measurement locations starting from a particular set of measurements, by minimizing the sensitivity of the parameter estimates with respect to observed response. The statistical properties of solution clusters generated from a Monte Carlo samples of noisy data are used to determine the best candidate measurement to be dropped from the current set. The process is repeated until solution sensitivity cannot be significantly reduced. We also show that the laborious Monte Carlo computations can be avoided in certain cases by using a direct computation of sensitivity estimates. A numerical example is provided to illustrate the method and to examine the performance of the proposed algorithm.     

道路交通流状态的多参数融合预测方法

针对道路交通流普遍存在的混沌特性以及单交通参数不足 以全面反映交通流状态的实际情况,考虑交通动力学系统中多个 交通参数之间的关联关系,提出一种新的多参数混沌时间序列预 测算法.该算法在相空间重构理论的基础上,借助Bayes估计将多个参数在 同一高维相空间中进行相点最优融合,从而增加重构相空间的系统信息量, 使得相空间的相点轨迹更加逼近原交通系统的动力学行为.同时借鉴单 参数混沌时间序列预测方法,从不同角度对动力学系统的运动状态进行描 述,以实现多参数时间序列的混沌预测.实验结果表明,通过融合多交通参数时 间序列,获得了更加完整的交通流状态变化特征.与单交通参数时间序列的预测 结果相比,其预测误差显著降低,均衡系数相应增大,提高了交通流状态预测的准确率.