fft_test

关于模拟退火的matalab原代码,真实可用(Matalab on simulated annealing source code，The real available)

OPQSK调制信号的载波相位/频率以及时间同步算法的实现与验证，很有使用价值(OPQSK modulated signal carrier phase/frequency and time synchronization algorithm and validation, very valuable)

matlab code for perceptron and adalin

FIR 底通和带通滤波器和IIR 底通和带通滤波器，多姿态，多角度，有不同光照,抑制载波型差分相位调制，这个有中文注释，看得明白，均值便宜跟踪的示例，使用大量的有限元法求解偏微分方程，感应双馈发电机系统的仿真，包含了阵列信号处理的常见算法。 ( Bottom-pass and band-pass FIR and IIR filter bottom pass and band-pass filter, Much posture, multi-angle, have different light, Suppressed carrier type differential phase modulation, The Chinese have a comment, understand it, Example tracking mean cheap, Using a large number of finite element method to solve partial differential equations, Simulation of doubly fed induction generator system, Contains a common array signal processing algorithm. )

在视图类CDemoView中映射“拉普拉斯锐化”事件的处理函数:OnLaplacienSharp()，以下是CDemoView:: :OnLaplacienSharp()函数的代码实现。(In the view class CDemoView in the mapping " Laplacian sharpening" event handler: OnLaplacienSharp (), following CDemoView::: OnLaplacienSharp () function code.)

一维光子晶体的FDTD 分析法MATLAB程序(One-dimensional FDTD analysis of photonic crystals MATLAB program)

SUBBAND CODING FOR SPEECH

system identification, m document based on idetification

基于Hough变换的人眼虹膜定位方法 MATLAB程序(A Method of Iris Location for Human Eye Based on Hough Transform and MATLAB Program)

测试可以跑，根据自己情况修改下函数即可. NSGA-III 首先定义一组参考点。然后随机生成含有 N 个（原文献说最好与参考点个数相同）个体的初始种群，其中 N 是种群大小。接下来，算法进行迭代直至终止条件满足。在第 t 代，算法在当前种群 Pt的基础上，通过随机选择，模拟两点交叉（Simulated Binary Crossover，SBX）和多项式变异 产生子代种群 Qt。Pt和 Qt的大小均为 N。因此，两个种群 Pt和 Qt合并会形成种群大小为 2N 的新的种群 Rt=Pt∪Qt。 为了从种群 Rt中选择最好的 N 个解进入下一代，首先利用基于Pareto支配的非支配排序将 Rt分为若干不同的非支配层（F1，F2等等）。然后，算法构建一个新的种群St，构建方法是从 F1开始，逐次将各非支配层的解加入到 St，直至 St的大小等于 N，或首次大于 N。假设最后可以接受的非支配层是 L层，那么在 L+ 1 层以及之后的那些解就被丢弃掉了，且 St FL中的解已经确定被选择作为 Pt+1中的解。Pt+1中余下的个体需要从 FL中选取，选择的依据是要使种群在目标空间中具有理想的多样性。(The test can run and modify the function according to its own situation. NSGA-III first defines a set of reference points. Then the initial population containing N individuals (preferably the same number of reference points as the original literature) was randomly generated, where N was the size of the population. Next, the algorithm is iterated until the termination condition is satisfied. On the basis of current population Pt, the algorithm simulates two-point crossover (SBX) and polynomial mutation to produce offspring population Qt by random selection.)