Eight-node conforming straight-side quadrilateral element with high-order completeness (QH8-C1)

A new eight-node conforming quadrilateral element with high-order completeness, denoted as QH8-C1, is proposed in this article. First, expressions for the interpolation displacement function satisfying the requirements for high-order completeness in the global coordinate system are constructed. Second, the displacement function expression in global coordinates is transformed into isoparametric coordinates, and the relationships between the two series of coefficients for the two kinds of displacement function expressions are found. Third, the displacement function expression is modified to satisfy the requirements of nodal freedom and interelement boundary continuity. The key to the new element construction is the derivation of the linear relationship expressions among 12 coefficients of element displacement interpolation polynomials in the global and isoparametric coordinate systems. As a result, the relationship between quadratic completeness and interelement continuity is explicitly given, and a proof of the completeness and the continuity was conducted to theoretically guarantee the validity of the derivation results. Furthermore, in order to verify the correctness of the theoretical work, nine numerical examples were performed. The computation results from these examples demonstrate that QH8-C1 exhibited excellent performance, including high simulation accuracy, fast convergence, insensitivity to mesh distortion, and monotonic convergence.     

LPNN-based approach for LASSO problem via a sequence of regularized minimizations

In compressive sampling theory, the least absolute shrinkage and selection operator (LASSO) is a representative problem. Nevertheless, the non-differentiable constraint impedes the use of Lagrange programming neural networks (LPNNs). We present in this article the -LPNN model, a novel algorithm that tackles the LASSO minimization together with the underlying theory support. First, we design a sequence of smooth constrained optimization problems, by introducing a convenient differentiable approximation to the non-differentiable -norm constraint. Next, we prove that the optimal solutions of the regularized intermediate problems converge to the optimal sparse signal for the LASSO. Then, for every regularized problem from the sequence, the -LPNN dynamic model is derived, and the asymptotic stability of its equilibrium state is established as well. Finally, numerical simulations are carried out to compare the performance of the proposed -LPNN algorithm with both the LASSO-LPNN model and a standard digital method.     

A Modified Variational-Gradient Method for Quasilinear Equations

A modified variational-gradient method is proposed and substantiated for quasilinear operator equations in a Hilbert space.     

Hammerstein系统参数的集员辨识

本文运用多输入多输出系统参数的集员辨识的结果讨论了Ｈａｍｍｅｒｓｔｅｉｎ系统参数的集员辨识．在系统动态噪声有界的假定下导出了Ｈａｍｍｅｒｓｔｅｉｎ系统参数的成员集是个椭球，并证明了在一定条件下椭球趋向于一点，文中还给出了仿真例子．     

L~∞（0，1）上一个WeakL~P（0，1）有关的格半范数

本文引进一个定义在Ｌ~∞（０，１）上格半范数ρｐ与一个熟知的格半范数ρ０关系，证明了在Ｌ~∞（０，１）中的序区间上，ρ０－拓扑与ρｐ－拓扑等价，从而证明了（Ｌ~∞（０，１），ρｐ）′的闭单位球是（Ｌ~∞（０，１），ρｐ）′的某个子集的弱－闭包，本文还证明了（Ｌ~∞（０，１），ρｐ）′可看作ＷｅａｋＬｐ（０，１）对偶空间的奇异部一个理想，而且这奇异部可由（Ｌ~∞（０，１），ρｐ）′与一算子族生成．（ｆ）ａｌｌ（ｌ．＼ｎ，ｏｏ（ｊ．）＝ｈｉｌｌｓｉｌｌ）ｔ￣．，ｉｆｆ￣＊（ｔ）ｒｅｓｐｅｃｔｉｖｅｌｙａｌｌ＇ｌ，Ｌ（ｑ，１）ｉｓｔｈｅｐｒｅｄｌｓｌａｌｏｆｔｙｒｅａｋＬＩ＇，ａｌｌｌｌｈ，ｒｌｌｏｌｌ－－ａｔ（，ｌｌｌｉ（＇ｌｌｌｅａｓｌｌｒｅａｌｇａ（ｌ￣ｓ，ａｒｅｐｒｅｓｅｌｌｔａ＇ｔｉｏｌｌｔｌｌｅｏｒｅｎｌｆｆ，ｒｅｌｅｌｌｌｅｌｌｔｓｉｌｌＳａｃ，ａｌｌ（ｌＳ，，．ｉｓｏｆ）ｔａｉｌｌｅ（ｌ．Ｈｏｗｅｖｅｒ，ｉｌｌ［２］，ｉｔｉｓｓｌｌｄｗｎｔｈａｔｔｉｌｅｒｅｐｒｅｓｅｌｌｔａｔｉｏｌｌｔｉｌｅｓｆ）ｒｅｌｌｌｏｆ）ｔａｉｌｌｅ（ｌｉｌｌ［．５］ｉｓｉｌｌ（ｔｏｒｒｅ（？ｔ．Ｉｎｔｈｉｓｎｏｔｅ，ｗｅｄｅ     

一类Boltzmann & Darwin混合寻优策略的收敛特性研究

基于文［１］提出的求解组合最优化问题的Ｂｏｌｔｚｍａｎｎ＆Ｄａｒｗｉｎ混合寻优策略，本文进一步分析研究了Ｂｏｌｔｚｍａｎｎ＆Ｄａｒｗｉｎ混合寻优策略非时齐情形下的收敛特性，包括全局收敛性和收敛速度。结果表明，Ｂｏｌｔｚｍａｎｎ＆Ｄａｒｗｉｎ混合寻优策略的收敛特性优于模拟退火（ｓｉｍ－ｕｌａｔｅｄ ａｎｎｅａｌｉｎｇ）方法的收敛特性。     

宽线性共轭梯度算法及其在盲多用户检测中的应用

本文提出了一种宽线性共轭梯度算法，分析了算法在均值意义下的收敛性，并给出了算法稳定条件。仿真表明，新算法应用于码分多址系统多址干扰抑制时的性能优于Yin（2003）及Schober（2004）所提出的算法。     

SPECTRAL DENSITY ESTIMATION FROM NONLINEARLY OBSERVED DATA

Abstract. The estimation of the spectral density function of a stationary Gaussian process at the input of an instantaneous nonlinearity is considered when the nonlinearity is known and a finite set of observations of the output process is given. A class of spectral estimates is considered and their quadratic-mean consistency is established; precise asymptotic expressions for their bias and covariance are derived and their asymptotic normality is obtained.