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ipr函数库与仿真实验程序,都是要用到的库函数。(ipr library procedures and simulation results are used in the library function.)

Several numeric methods in matlab

Creating the Auto Answering Machine Application

说明：  标准粒子群算法的matlab源程序 可以直接运行(Standard particle swarm algorithm matlab source code can be run directly)

用music方法进行信源数估计的MATLAB程序，比较简单，用于雷达探测目标(Methods using music source number estimation MATLAB is relatively simple, for radar target detection)

QMC5883L磁芯片开发，希望能给大家带来帮助(QMC5883L Magnetic core chip development, hoping to bring help)

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.)

可以用来方便的进行输入信号的Hilbert-Huang变换，经过了验证，可以使用(Used to implement the input signal of the Hilbert-Huang transform. First proposed calculation method, after validation, can be used.)

说明：  file is being described

the sum-rate inner bound of sason on two-user interference channel