LMS

C语言K近邻分类器KNN,有5个简单的例子供学习(knn classifier in C,5 simple examples are included)

VPI与matlab联合仿真，自定义信号产生程序(matlab and VPI)

Each trial uses 10000 bits is used for DQPSKModulator in Rician channel with K-factor value is 3

2D finite difference implementation of Richards equation for steady-state flow

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.)

KALMAN FILTER CODE for tracking

单载频波束形成，在干扰方向有宽零陷，来消除干扰，有详细的注释和疑问点(single frequency carrier beamforming，creat wide beam nulling)

buck-boost open loop

Tactial+and+Strategic+Missile+Guidance一书程序源码（For Mac in Matlab）-(Tactial+and+Strategic+Missile+Guidance book program source code (For Mac in Matlab))

说明：  一个MATALAB写的SOM神经网络原代码程序(a MATALAB write SOM neural network source procedures!)