PVARRAY_BATTERY_DOUBLE_DCDC

matlab最速下降法，简单的求二次函数的hesse矩阵(steep decent method)

基于无差拍的有源电力滤波器（APF）的仿真设计，包括（谐波检测， 电流跟踪环节） Z域(spwm调制)(Deadbeat based Active Power Filter (APF) simulation design, including (harmonic detection, current tracking links) Z domain (spwm modulation))

从已经出现故障的信号中，提取能够反映该信号故障特征的值，以后就能根据数值直接判断别的信号有没有出现该故障，更加直观(Signals the fault has appeared in the fault feature extraction and can reflect the value of signal, after can according to numerical directly to judge the other signals have appeared the fault, more intuitive)

for estimating unknown parameters using ARIMA

采用二进制编码方法，利用遗传算法求函数的极小值。给出源程序，并加注释。(Using the binary coding method, the genetic algorithm is used to find the minimum value of the function. Give the source code and add annotations.)

y3k=fft(u,(m+n-2)/4) i=1:(m+n-2)/4 subplot(5,2,9) stem(i,u) title( 滤波后上采样 ) k=1:(m+n-2)/4 subplot(5,2,10) stem(k,y3k) title( 上采样频谱 ) xlabel( k ) ylabel( y3k ) (y3k = fft (u, (m+ n-2)/4) i = 1: (m+ n-2)/4 subplot (5,2,9) stem (i, u) title (after filtering the sample) k = 1: (m+ n-2)/4 subplot (5,2,10) stem (k, y3k) title (on the sample spectrum) xlabel (k) ylabel (y3k))

一个关于通过matlab操作和编辑Word文档的例子(matlab Word)

这个文章系统详细的介绍了 非线性模型的一种确定P的阶数的方法(This article describes in detail the system as a nonlinear model to determine the order of method P)

本程序可以用于解算各个扫面点的距离,以得到相应的座标点(compute the distance of scan point)

基于新的树编码方式用免疫遗传算法解决DCMST问题：提出了一种新的树编码方式，可以方便地表达一棵树，简化了树在遗传算法中的编码表达。且新的树编码方式清楚地表达了边的信息，有利于疫苗的设计。此外，本文使用免疫遗传算法，有效地克服了传统遗传算法中解退化的现象。数值实验表明，解的振荡相对于传统遗传算法减小了，且收敛速度更快。(New tree-based encoding genetic algorithm with immune DCMST problem: A new tree encoding, you can easily express a tree, the tree simplifies the code in the expression of genetic algorithm. And new tree encoding side clearly expressed information is conducive to vaccine design. In addition, we use the genetic algorithm can effectively overcome the traditional genetic algorithm, the phenomenon of degradation in the solution. Numerical experiments show that the oscillation of solutions with respect to reducing the traditional genetic algorithm, and the convergence faster.)