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MVDR一体化算法程序，用于GPS信号处理(MVDR algorithm program integration for GPS signal processing)

本章先分析说明伪谱法求解波动方程的算法原理，再给出弹性波场伪谱法数值模拟的改进算法；然后，通过直接引入P波波场变量和S波波场变量，给出可实现弹性波场的P波和S波分解的波动方程，并用伪谱法实现弹性波场的P波和S波分解的数值模拟；最后用伪谱法对井间地震的复杂波场作数值模拟，分析井间地震波场中各种波的传播规律。(In this chapter, the principle of pseudo-spectral method for solving wave equation is analyzed, and then an improved algorithm for numerical simulation of elastic wave field is given. Then, by directly introducing P-wave field variable and S-wave field variable, the wave equation which can realize P-wave and S-wave decomposition of elastic wave field is given, and the P-wave sum of elastic wave field is realized by pseudo-spectral method. Numerical simulation of S-wave decomposition; finally, pseudo-spectral method is used to simulate the complex cross-well seismic wave field and analyze the propagation law of various waves in cross-well seismic wave field.)

NP是美国匹兹堡大学的T.L.Saaty 教授于1996年提出了一种适应非独立的递阶层次结构的决策方法，它是在网络分析法（AHP）基础上发展而形成的一种新的实用决策方法。其关键步骤有以下几个： 1 确定因素，并建立网络层和控制层模型。 2 创建比较矩阵。 3 按照指标类型针对每列进行规范化。 4 求出每个比较矩阵的最大特征值和对应的特征向量。 5 一致性检验。如果不满足，则调整相应的比较矩阵中的元素。 6 将各个特征向量单位化(归一化)，组成判断矩阵。 7 将控制层的判断矩阵和网络层的判断矩阵相乘，得到加权超矩阵。 8 将加权超矩阵单位化(归一化)，求其K次幂收敛时的矩阵。其中第j列就是网络层中各元素对于元素j的极限排序向量。 (NP is a professor at the University of Pittsburgh TLSaaty presented in 1996, an adaptation of non-independent Hierarchy of decision-making method, which is the analytic network process (AHP) formed on the basis of the development of a new and practical decision-making method . The key steps are the following: A determining factor, and a network layer and control layer model. 2 create a comparison matrix. For each of the three types of indicators in accordance with normalized columns. 4 find the maximum for each comparison matrix eigenvalue and the corresponding eigenvectors. 5 consistency test. If not satisfied, then the comparison to adjust the corresponding matrix elements. 6 will each feature vector units of (normalized), to determine the composition of matrix. 7 to determine the control layer and network layer to determine matrix matrix multiplication, to be weighted super-matrix. 8 of the weighted super-matrix units of (normalized), seeking the powe)

PCA用于交叉验证确定维数，很有用的程序，希望对你有用(PCA to determine the number of dimensions for cross-validation, very useful program, you want to be useful)

对雷诺方程采用有限差分法来求解 使用matlab程序(For Reynolds equation, the finite difference method is used to solve the matlab program.)

Application of spread spectrum for satellite land mobile systems is difficult in satellite link because of synchronization and providing code or considerable Doppler shift. This is particularly high for LEO satellites. So that, a Doppler shift carrying a frequency in band S can reach to several tens of kHz. To estimate and compensate Doppler, a CW pilot carrier is used. The aim of this study is simulate Doppler frequency estimation using MIMO-OFDM technique. Finally, the proposed algorithm is compared to MMSE algorithm in order to insure its performance.

潮流+寻找联络开关的Matlab程序，希望对你有用(Trend+ looking for contact switch)

四旋翼飞行器嵌套饱和控制算法的matlab实现程序(quadrotor helicopter)

摄影测量小程序，实现后方交会功能，包括了普通方法和角锥法两种方式(Photogrammetry small program to achieve resection, including a common approach and angle cone method in two ways)

极限学习机（extreme learning machine）ELM是一种简单易用、有效的单隐层前馈神经网络SLFNs学习算法。2006年由南洋理工大学黄广斌副教授提出。传统的神经网络学习算法（如BP算法）需要人为设置大量的网络训练参数，并且很容易产生局部最优解。极限学习机只需要设置网络的隐层节点个数，在算法执行过程中不需要调整网络的输入权值以及隐元的偏置，并且产生唯一的最优解，因此具有学习速度快且泛化性能好的优点。(Extreme Learning Machine (extreme learning machine) ELM is an easy-to-use and effective single hidden layer feedforward neural network the SLFNs learning algorithm. 2006 by the Nanyang Technological University Associate Professor Huang Guangbin. Traditional neural network learning algorithm (BP) artificial network training parameters, and it is easy to generate a local optimal solution. Extreme Learning Machine network only need to set the number of hidden nodes, the algorithm implementation process does not need to adjust the network input weights and hidden element of bias, and only optimal solution, so the learning speed and generalization good performance advantages.)