ICP_14900012

现有的贪婪迭代类压缩感知重构算法均基于最小二乘对信号进行波形估计，未考虑到可能将量测噪声引入信号估计的情况．针对以上不足，提出了一种基于线性Kalman滤波的压缩感知弱匹配去噪重构算法(Existing greedy iterative reconstruction algorithms class compressed sensing the signal waveform based on least squares estimation does not take into account the possible introduction of the measurement noise signal estimation situation. For the above shortcomings, we propose a linear Kalman filter based on compressed sensing reconstruction algorithm denoising weak match)

模糊控制的C语言实现，双输入单输出，采用查表法进行模糊推理，去模糊方法采用重心法，易于单片机实现(C language realization of fuzzy control, double input and single output, using look-up table method for fuzzy reasoning, using center of gravity method for deblurring, easy to realize by single chip computer)

层次分析法确定权重必备，是一款实用的小软件(AHP to determine the weights necessary, is a small software utility)

将一些标准数值与非数值算法集中封装在几个DLL中,需要用时可在VB/Delphi/C#中分别调用,这里提供了3个小程序,分别用于线性方程组求根、计算行列式的值、矩阵求逆。(Some standard numerical and non-numerical algorithm focus on packaging in several DLL, it is necessary to use when in VB/Delphi/C# Call, respectively, this provides three small programs, respectively, for the roots of linear equations, calculation of the value of the determinant, matrix inversion.)

WRF模拟结果气象要素的检验对比，包含均方根误差，偏差和相关系数等(WRF simulation test results comparing meteorological elements, including the root mean square error, deviations and correlation coefficients)

矩阵按键程序，用的是C。本人还是学生，做得不好请大家多多指教(Matrix button procedure is used in C. I am still a student, doing a lot of bad advice please)

矩阵求逆，使用fortran语言编写，希望能对有需要的朋友有所帮助(Matrix inversion, written in FORTRAN language, hoping to help friends in need)

数值分析非线性方程求解（五种方法）。分析牛顿法、割线法、对分法、Steffensen法、简易牛顿法解线性方程组的性质(Numerical analysis of nonlinear equations (five methods). Nature of the analysis of Newton, secant, bisection method, Steffensen method, simple Newton method for solving linear equations)

C语言实现大数的加法减法以及乘法,水平限于仅仅大学生水平学习使用。(Use C language to achieve that addition, subtraction, and multiplication of a big intger.)

％ 奇异值分解 (sigular value decomposition,SVD) 是另一种正交矩阵分解法；SVD是最可靠的分解法， ％ 但是它比QR 分解法要花上近十倍的计算时间。[U,S,V]=svd(A)，其中U和V代表二个相互正交矩阵， ％ 而S代表一对角矩阵。 和QR分解法相同者， 原矩阵A不必为正方矩阵。 ％ 使用SVD分解法的用途是解最小平方误差法和数据压缩。用svd分解法解线性方程组，在Quke2中就用这个来计算图形信息，性能相当的好。在计算线性方程组时，一些不能分解的矩阵或者严重病态矩阵的线性方程都能很好的得到解( Singular value decomposition (sigular value decomposition, SVD) is another orthogonal matrix decomposition method SVD decomposition is the most reliable method, but it takes more than QR decomposition near ten times the computing time. [U, S, V] = svd (A), in which U and V on behalf of two mutually orthogonal matrix, and the S on behalf of a diagonal matrix. And QR decomposition are the same, the original matrix A is no need for the square matrix. The use of SVD decomposition method are used as a solution of least squares error method and data compression. Using SVD decomposition solution of linear equations, in Quke2 on to use this information to calculate the graphics performance quite good. In the calculation of linear equations, some indecomposable matrix or serious pathological matrix of linear equations can be a very good solution)