Multi-Tier-Application-with-Tables

利用MATLAB实现PSK系统的仿真 很实用(PSK systems using MATLAB simulation to achieve practical)

simulation of distributed power flow controller...

采用遗传算法分配微电网微源的潮流调度，从而实现微电网能量优化(Using Genetic Algorithm to Distribute Power Flow Scheduling of Microgrid Micro Sources to Realize Microgrid Energy Optimization)

This is the file load flow in power system

xPC Target 工具箱使你可以在 Simulink 的框图中加入 I/O 方块图，并用 RTW 产生代码，最后下载到另一个运行 xPC Target 实时内核的 PC 机上。对于控制和 DSP 系统来说 xPC Target 是理想的快速原型和硬件在回路测试工具，它可以使你在一台标准的 PC 机上运行实时模型。如果附加 xPC Target 嵌入模块选项，你可以把你的实时嵌入式系统放入到一台微机上，应用于生产、数据采集、标定和测试应用程序等过程中。 (xPC Target toolbox allows you to add in the Simulink block diagram of the I/O block diagram, and use RTW generated code, and finally downloaded to another running xPC Target real-time kernel of the PC machine. For the control and DSP systems is ideal for the xPC Target rapid prototyping and hardware in the loop testing tool that allows you to a standard PC, running real-time model. If the additional xPC Target Embedded Module option, you can put your real-time embedded systems to a microcomputer applied to the production, data acquisition, calibration and test applications such as process. )

光学工程很有用的有限元优化问题解，能轻松快速的进行工程类的和光学类的优化处理(Useful in optical engineering finite element solutions of optimization problems can be quickly and easily and optical engineering optimization class)

自举是一种由重采样估计，独立和（蒙特卡洛重采样）等概率设置一个单一的数据统计变化的一个途径。允许的措施估计那里的潜在分布是未知的或者样本量很小。他们的结果与这些分析方法的统计特性相一致。 在这里，我们使用非参数逼近。非参数引导更简单。它不使用该模型的结构，建造人工数据。矢量[易西]是重采样，而不是直接与replecement。这些参数是从这些对构建。 二，回归模型时，应使用在回归方程中的两个变量是随机的，会有错误的，即不是由研究者控制。模式，我用普通最小二乘回归低估了变量之间的错误时，他们都含有线性关系的斜率。据索卡尔和罗尔夫（1995），模型二回归的主题是一对哪些研究和争论仍在继续，最终建议是很难做出。 BOOTGMREGRESS模型II是一个引导程序。这需要s引导和规范样品前坡计算变量。这两个变量的每个转化为具有零均值和标准差的一个。由此产生的斜率是线性回归系数的Y在X里克创造了这个词（1973）几何平均数并给出了一个模型II回归广泛审查。它也被称为引导标准的主要轴线(The bootstrap is a way of estimating the variability of a statistic from a single data set by resampling it independently and with equal probabilities (Monte Carlo resampling). Allows the estimation of measures where the underlying distribution is unknown or where sample sizes are small. Their results are consistent with the statistical properties of those analytical methods. Here, we use the Non-parametric Bootstrap. Non-parametric bootstrap is simpler. It does not use the structure of the model to construct artificial data. The vector [yi, xi] is instead directly resampled with replecement. The parameters are constructed from these pairs. Model II regression should be used when the two variables in the regression equation are random and subject to error, i.e. not controlled by the researcher. Model I regression using ordinary least squares underestimates the slope of the linear relationship between the variables when they both contain error. According to Sokal and Rohlf (1995), t)

In this paper a Conventional PD Controller and Fuzzy Neural Controller for trajectory tracking of mobile robot platform is presented. Using computed torque method is given input- torque on mobile robot wheels. The locomotion control structure is based on integration of kinematics and dynamics model of mobile robot base. The proposed Control scheme and Fuzzy Neural Algorithm could be useful for building an autonomous non-destructive testing system based on wheeled mobile robot. Fuzzy Neural Network-Rule base, is optimized using Genetic Algorithm. Simulations are done using Matlab software. Key words:Mobile Robot, Conventional, Fuzzy Logic, Neural Network, Genetic Algorithm, trajectory tracking,

运用差分进化算法（DE）优化分数阶pid控制器(Use of differential evolution algorithm (DE) optimization of fractional order pid controller)

Analytic Model for Gain Modulation in EDFAs