电磁干扰（EMI）自动测试系统主控计算机的替代与测试软件的移植

本文介绍了使用通用微机来替代ＨＰ主控机的实例。其通用微机内配有成都电子科技大学研制的ＥＳ１４００型ＧＰＩＢ接口卡。分别用ＱｕｉｃｋＢＡＳＩＣ和ＢｏｒｌａｎｄＣ＋＋语言编制了两套ＥＭＩ测试程序，ＱｕｉｃｋＢＡＳＩＣ版ＥＭＩ是在ＤＯＳ环境下运行，ＢｏｒｌａｎｄＣ＋＋版ＥＭＩ在Ｗｉｎｄｏｗｓ环境下运行。运行结果十分令人满意，替代完全成功，经济效益显著。     

在MATLAB环境下创建C/C++外部应用程序的方法

介绍了利用ＭＡＴＬＡＢ编译器和ＭＡＴＬＡＢ的Ｃ／Ｃ＋＋Ｍａｔｈ函数库以及Ｃ／Ｃ＋＋编译器生成Ｃ／Ｃ＋＋应用程序的优点及具体的编译链接流程,并通过实例分析了创建Ｃ／Ｃ＋＋应用程序的方法。     

C＋＋面向对象使用扩展内存和扩充内存

本文详细介绍了ＩＢＭ微型计算机内存的分类，在程序中使用扩展内存和扩充内存的方法，利用Ｃ＋＋面向对象使用ＸＭＳ、ＥＭＳ、ＵＭＢ、ＨＭＡ的方法，并且给出了源程序。     

硬盘驱动器抗冲击性能的分析与计算

由于采用浮动磁头机构，冲击产生头盘碰撞始终是硬盘驱动器要解决的重大课题，尤其是在恶劣环境条件下工作的硬盘驱动器的抗冲击性能更是至关重要。本文对硬盘驱动器的抗冲击性能进行了理论分析和计算机仿真，用ＶＣ＋＋及ＭＡＴＬＡＢ语言编制了头盘冲击特性计算程序，对硬盘驱动器工作状态下头盘系统的冲击特性进行了理论计算。解决了硬盘驱动器抗冲击的理论计算问题。     

受限外部信息源计算的P—NP性质

本对Ｏｒａｃｌｅ图灵机在接受计算中的查询次数加以限制，并且得到结果：存在无穷多个非多等价的递归集Ａ，Ｂ，Ａ＇，Ｂ＇，Ａ＇＇，Ｂ＇＇，Ａ＇＇＇，Ｂ＇＇＇，它们满足性质：Ｐ（Ａ，ｑ）＝Ｐ（Ａ，ｑ＋１），Ｐ（Ｂ，ｑ）≠Ｐ（Ｂ，ｑ＋１），ｐ（Ａ＇，ｑ）＝Ｐ（Ａ＇），Ｐ（Ｂ＇，ｑ）≠Ｐ（Ｂ＇），ＮＰ（Ａ＇＇，ｑ＋１），ＮＰ（Ｂ＇＇，ｑ）≠ＮＰ（Ｂ＇＇，ｑ＋１），ＮＰ（Ａ＇＇，ｑ）＝ＮＰ（Ａ＇＇），ＮＰ（Ｂ     

FORTRAN程序调用MATLAB引擎实现计算可视化

探索了ＦＯＲＴＲＡＮ程序调用ＭＡＴＬＡＢ函数的基本原理,用实例介绍了调用ＭＡＴＬＡＢ引擎函数,实现ＦＯＲＴＲＡＮ程序计算结果的可视化过程。并给出如何编译带有ＭＡＴＬＡＢ引擎函数ＦＯＲＴＲＡＮ程序的具体技术。     

人工智能在装修配色领域的应用

本文简述建筑装修ＣＡＤ的现状，分析了装修ＣＡＤ系统中应用人工智能的必要性，阐述了用面向对象的Ｃ＋＋语言研制的卧室装修配色专家系统ＢＣＭＢＳ的原理。该系统方便实用，界面友好，易于扩充和维护，并带有专用推理系统。     

Windows环境下应用MATLAB的软件开发技术

本文介绍当前风行美国的科学工程计算语言ＭＡＴＬＡＢ的特点，阐述了在Ｗｉｎｄｏｗｓ环境下应用ＭＡＴＬＡＢ进行二次开发的设计方法，并给出了一个应用ＭＡＴＬＡＢ实现ＣＡＩ软件的实例．     

用IBMPC／XT和8098单片机实现多点温度遥测

本文介绍了如何用ＩＢＭＰＣ／ＸＴ和８０９８单片机通过半双工异步通讯方式所实现的远距离多点测试。软件采用ＢＡＳＩＣＡ，８０８８汇编语言和８０９８汇编语言进行混合编程，从而提高了采样速度和精度。     

NPB CG在分布式环境下的并行实现

ＣＧＢｅｎｃｈｍａｒｋ是ＮＡＳＰａｒａｌｅｌＢｅｎｃｈｍａｒｋｓ（ＮＰＢ）中的一个核心程序，它用共轭梯度法求大型稀疏对称正定矩阵的最小特征值，本文介绍其主要算法，并给出在分布式环境下的高效并行算法，最后给出了在ＳＧＩＣｈａｌｅｎｇｅＰＶＭ平台上的测试结果     

A comparison of the balanced matrix method and the aggregation method of model reduction

The balanced matrix method and the aggregation method for model reduction are compared. It is shown that there is a "natural" choice for the aggregated reduced model output matrix that makes the aggregated model comparable to the balanced matrix reduced-order model. This assumes that the eigenvalues retained in the aggregated model are truly dominant and that the orders of the two models are equal. However, there are situations in which the choice of the eigenvalues to be retained in an aggregated model is not obvious. In these cases the balanced matrix method may be superior. The models are compared in two numerical examples.     

基于融合邻域寻优与θ-PSO算法的矩阵特征值求解

提出一种融合邻域寻优与θ-PSO算法的矩阵特征值求解新方法,将矩阵特征值的求解问题转化为最优化问题。与需要多次运行程序分别求解不同范围的特征值算法相比,该方法可以一次性求出矩阵的全部特征根。仿真实验表明,该算法编程实现方便,对于不同类型的矩阵均可以应用,求解精度高,收敛速度快,大概在10~15代左右就可以收敛,完全可以满足工程实践运算中对精度和速度的要求。     

Halley's method for the matrix sector function

The matrix n-sector function is a generalization of the matrix sign function; it can be used to determine the number of eigenvalues of a matrix in a specific sector of the complex plane and to extract the eigenpairs belonging to this sector without explicitly computing the eigenvalues. It is known that Newton's method, which can be used for computing the matrix sign function, is not globally convergent for the matrix sector function. The only existing algorithm for computing the matrix sector function is based on the continued fraction expansion approximation to the principal nth root of an arbitrary complex matrix. In this paper, we introduce a new algorithm based on Halley's generalized iteration formula for solving nonlinear equations. It is shown that the iteration has good error propagation properties and high accuracy. Finally, we give two application examples and summarize the results of our numerical experiments comparing Newton's, the continued fraction, and Halley's method     

一种求解复Hermite矩阵特征值的方法

介绍几种求解矩阵特征值和特征向量的经典算法及各自优缺点,通过理论推导,提出了一种性能稳健的方法,可以求解信号处理中常见的复Hermite阵.将对复Hermite矩阵求特征值和特征向量的问题转化为求解实对称阵的特征值和特征向量,而实对称阵的求解采用一种改进的三对角Householder法.最后把结果与Matlab仿真结果比较,可以看出该方法有很高的精确度.     

A strategy for detecting extreme eigenvalues bounding gaps in the discrete spectrum of self-adjoint operators

For a self-adjoint linear operator with a discrete spectrum or a Hermitian matrix, the “extreme” eigenvalues define the boundaries of clusters in the spectrum of real eigenvalues. The outer extreme ones are the largest and the smallest eigenvalues. If there are extended intervals in the spectrum in which no eigenvalues are present, the eigenvalues bounding these gaps are the inner extreme eigenvalues.We will describe a procedure for detecting the extreme eigenvalues that relies on the relationship between the acceleration rate of polynomial acceleration iteration and the norm of the matrix via the spectral theorem, applicable to normal matrices. The strategy makes use of the fast growth rate of Chebyshev polynomials to distinguish ranges in the spectrum of the matrix which are devoid of eigenvalues.The method is numerically stable with regard to the dimension of the matrix problem and is thus capable of handling matrices of large dimension. The overall computational cost is quadratic in the size of a dense matrix; linear in the size of a sparse matrix. We verify computationally that the algorithm is accurate and efficient, even on large matrices.     

Lyapunov's matrix equation with system matrix in companion form

A simple method for solving Lyapunov's matrix equation for linear continuous systems with the system matrix in companion form is proposed. The method involves the inversion of the Hurwitz matrix. A necessary and sufficient condition for the existence of a solution to the equation is also obtained. This condition applies even in the critical case where the sum of two eigenvalues of the system matrix is zero.     

Preconditioned iterative methods for the large sparse symmetric eigenvalue problem

In general, two strategies are used in methods for the solution of large sparse eigensystems. The former are transformation and elimination methods which may change the structure of the original matrix, destroy sparsity and are only suitable if all or most of the eigenvalues are required. The latter includes methods which are iterative and no change in the structure of the original matrix occurs. These methods are often employed when one or several of the eigenvalues are required. In this paper we study iterative methods whereby the extreme eigenvalues and their corresponding eigenvectors are evaluated by a new preconditioning method which with a suitable choice of shift of origin and preconditioning parameter produces a powerful convergent method to cope with problems in this class.     

Origin-shifted algorithm for matrix eigenvalues

In this paper an origin-shifted algorithm for matrix eigenvalues based on Frobenius-like form of matrix and the quasi-Routh array for polynomial stability is given. First, using Householder's transformations, a general matrix A is reduced to upper Hessenberg form. Secondly, with scaling strategy, the origin-shifted Hessenberg matrices are reduced to the Frobenius-like forms. Thirdly, using quasi-Routh array, the Frobenius-like matrices are determined whether they are stable. Finally, we get the approximate eigenvalues of A with the largest real-part. All the eigenvalues of A are obtained with matrix deflation. The algorithm is numerically stable. In the algorithm, we describe the errors of eigenvalues using two quantities, shifted-accuracy and satisfactory-threshold. The results of numerical tests compared with QR algorithm show that the origin-shifted algorithm is fiducial and efficient for all the eigenvalues of general matrix or for all the roots of polynomial.     

Computation of eigenvalues of a real matrix

This paper presents a computational procedure for finding eigenvalues of a real matrix based on Alternate Quadrant Interlocking Factorization, a parallel direct method developed by Rao in 1994 for the solution of the general linear system Ax=b. The computational procedure is similar to LR algorithm as studied by Rutishauser in 1958 for finding eigenvalues of a general matrix. After a series of transformations the eigenvalues are obtained from simple 2×2 matrices derived from the main and cross diagonals of the limit matrix. A sufficient condition for the convergence of the computational procedure is proved. Numerical examples are given to demonstrate the method.     

Eigenfactor solution of the matrix Riccati equation--A continuous square root algorithm

This paper introduces a new algorithm for solving the matrix Riccati equation. Differential equations for the eigenvalues and eigenvectors of the solution matrix are developed in which their derivatives are expressed in terms of the eigenvalues and eigenvectors themselves and not as functions of the solution matrix. The solution of these equations yields, then, the time behavior of the eigenvalues and eigenvectors of the solution matrix. A reconstruction of the matrix itself at any desired time is immediately obtained through a trivial similarity transformation. This algorithm serves two purposes. First, being a square root solution, it entails all the advantages of square root algorithms such as nonnegative definiteness and accuracy. Secondly, it furnishes the eigenvalues and eigenvectors of the solution matrix continuously without resorting to the complicated route of solving the equation directly and then decomposing the solution matrix into its eigenvalues and eigenvectors. The algorithm which handles cases of distinct as well as multiple eigenvalues is tested on several examples. Through these examples it is seen that the algorithm is indeed more accurate than the ordinary one. Moreover, it is seen that the algorithm works in cases where the ordinary algorithm fails and even in cases where the closed-form solution cannot be computed as a result of numerical difficulties.