PSO

能够非常有效地在比较大的干扰下比较准确地反映真实值，如果观测端加入干扰较大时，卡尔曼滤波器能够较为有效地进行滤波，不过当状态端的干扰增大时，卡尔曼滤波器的滤波效果也会随之下降。(Can very effectively in the relatively large disturbances under more accurately reflect the true value, if the observation end joining interference is large, Calman filter can effectively filter, but when increasing the jamming state end when the filter effect of Calman filters will fall.)

of the electrical power system. At the time of transformer energization high current is drawn by the transformer known as the inrush current. This current is nearly ten times more than the full load current of transformer. It produces mechanical stress on transformer and also affects the windings and bushings of the transformer. The large switching transient current affects malfunction of protection system of power system and the different equipments connected to system. So the inrush current should be minimized. There are different methods used to minimize the inrush current such as volt second balance, series compensator, point on wave switching method etc. This paper focuses on point on wave switching method. It also explains the results for inrush current of three phase transformer with and without point on wave switching method. A test is driven on 450 kVA, 500kV/230kV grounded Y/D transformer in MATLAB/SIMLINK environment. It also focuses on

在matlab上实现各自函数傅立叶变换的小程序，仅供参考。(In matlab to achieve their respective functions Fourier transform applet for reference only.)

simulink建模与仿真 基础入门源代码(simulink modeling and simulation based on the source code entry)

Supermarket cashier system .doc(Supermarket cashier system )

仿真了OFDM_UWB系统，调制方式采用QPSK，参数设置完全符合FCC标准(Simulation of a transmitter implementing the OFDM transmission chain with QPSK modulation on each sub-carrier)

灰度扩展及灰度图像伪彩色化：灰度值在0～20之间显蓝色，20～40之间显绿色，40～255显红色。 (Gray-scale images of pseudo-color: gray-scale value between 0 to 20 Hin blue, between 20 to 40 green, 40 ~ 255 marked in red.)

fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!. Given the samples of a laplace distribution, the PDF parameter is found fits data to the probability of the form: p(x) = 1/(2*b)*exp(-abs(x-u)/b) with parameters: u,b format: result = fit_ML_laplace( x,hAx ) input: x - vector, samples with laplace distribution to be parameterized hAx - handle of an axis, on which the fitted distribution is plotted if h is given empty, a figure is created. output: result - structure with the fields u,b - fitted parameters CRB_b - Cram?r-Rao Bound for the estimator value RMS - RMS error of the estimation type - ML

This has minesweeper code

matlab中的数据分析（回归），包括相应的ppt，以及例子的matlab程序源码，便于方便学习(Matlab in the data analysis (return), including the corresponding PPT, and examples of matlab source, easy for learning)