some-CFS--function

this is the simulation designed to know the switching time of the svpwm and the simulink model of the 6-pulse invetre and induction model with the krause d-q transformation is simulated .......

在计算机上产生一组实验数据，首先产生一段零均值白噪声数据u（n）,令功率为 ，让u(n)通过一个三阶FIR： 得到y(n). .y(n)上加三个实正弦信号f1’=0.1,f2’=0.25,f3’=0.26调整 和正弦信号幅度信噪比大致为10dB,50dB,50dB. (1) 令N=256,描绘xn波形； (2)得出真实功率谱密度 . (3) 利用此实验数据Pisarenko谐波分解法估计该实验数据的正弦频率及幅度。 (On the computer to generate a set of experimental data, first generate a zero mean white noise data u (n), so that power, let u (n) through a third-order FIR: get y (n).. Y (n) added three real sinusoidal signal f1 ' = 0.1, f2' = 0.25, f3 ' = 0.26 and sine signal amplitude adjustment signal to noise ratio is approximately 10dB, 50dB, 50dB. (1) that N = 256, depicted xn waveform (2) the true power spectral density obtained. (3) using this experimental data, Pisarenko harmonic decomposition method to estimate the experimental data of the sinusoidal frequency and amplitude.)

matllab有限元函数求值程序关于3D四面体网格(a program library which evaluates a finite element function defined on a 3D tetrahedral mesh)

求均值 方差 标准差 用matlab编写的 (Average variance standard deviation using matlab)

Prueba1 FletcherReeves Hernández Santiago José Maestría en Ciencias de la Computación Septiembre / 2011 1. Comenzar con un punto arbitrario 2. Calcular Gradiente de Fi 3. Si el Gradiente Fi es igual a 0(converge), termina 4. Si el Gradiente Fi es !=0 continuar 5. Encontrar dirección de búsqueda Si= -GradienteFi= - Gradiente F(Xi) 6. Determinar la Longitud Optima del incremento lamda(i) en dirección Si X(i+1)=X(i)+lamda(i)*S(i)= X(i)-lamda(i)*Gradiente F(Xi) 7. Hacer i=2 8. Obtener Gradiente Fi 9. Calcular Si= -GradFi + ( [abs(GradFi)^2]/[abs(GradF(i-1))^2] )*S(i-1) 10. Determinar la Longitud Optima del incremento lamda(i) en dirección Si X(i+1)=X(i)+lamda(i)*S(i)= X(i)-lamda(i)*Gradiente F(Xi) 7. Verificar Optimalidad de X(i+1) Si es optimo, detener Si no es optimo hacer i=i+1 e ir al paso 8

分水岭分割算法的MATLAB实现，本人测试过很好用(wavelet segmentation MATLAB code )

Generalization ability of extreme learning machine with uniformly ergodic Markov chains

This M Code simulate the effects of clipping and filtering on PAPR PAPR reduction in OFDM-based wireless standard.Also the performance of BER versus SNR of original OFDM signals with PAPR reduction based on clipping and filtering schemes over AWGN channel is evaluated. The simplest and most widely used technique of PAPR reduction is to basically clip the part of signals that are outside the allowed region.Clipping methode introduces both in band distortion and out of band radiation into OFDM signals which degrades the performance including the BER and spectral efficiency.Filtering can reduce out of band radiation after clipping although it can not reduce in-band radiation distortion.However, clipping may cause some peak regrowth so that the signal after clipping and filtering will exceed the clipping level at some point.To reduce peak regrowth a repeated filtering and clipping operation can be used to obtain a desirable PAPR at a cost of computational complexity increase.

一份关于单个目标跟踪和多个目标跟踪的KALMAN滤波资料,详细的解释个多种算法 (A report on the single target tracking and multiple target tracking KALMAN filtering information, a detailed explanation of a multiple target )

让初学者可以更加容易了解LDA算法的基本运行过程(For beginners can more easily understand the basic operation process of LDA algorithm)