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使用iir方法设计的数字滤波器，通过验证，功各位使用。(Designed using iir digital filter, through verification, power you use.)

matlab实现函数y=sin(pi*x)的二次平方逼近的脚本语言，(matlab to achieve the function y = sin (pi* x) approximation of the second square of the scripting language,)

用matlab仿真模拟骑车功能，左右转向，油门，历程功能。(Simulation ride, about steering, throttle, the course features)

说明：  均匀线阵上实现DOA估计，ESPRIT算法，16阵元（可调），半波长阵列流形。(DOA estimation on a uniform linear array, ESPRIT algorithm, 16 array elements (adjustable), half-wavelength array manifold.)

matlab code for direction finding algorithm of MUSIC

该文件中包含有常微分方程初值问题的四种数值解法，分别是：RK4法，AB4法，AB4_AM4预测矫正法和改进的AB4_AM4法(The document contains the initial value problem of ordinary differential equations of the four numerical method, namely: RK4 method, AB4 Act, AB4_AM4 forecast correction method and improved method AB4_AM4)

说明：  该程序试图对具有31个城市的VRP进行求解，已知的最优解为784.1，我用该程序只能优化到810左右，应该是陷入局部最优，但我不知问题出在什么地方。请用过蚁群算法的高手指教。 蚁群算法的matlab源码，同时请指出为何不能优化到已知的最好解(The program attempts to have 31 cities of the VRP is solved, the optimal solution is known as 784.1, I use the program can only be optimized to about 810, should fall into local optimum, but I do not know where the problem is. Please teach master ant colony algorithm used. Ant colony matlab source, also please explain why not optimize the best solution to a known)

雷达信号处理算法，MTI、MTD、PD相关仿真。(Radar signal processing algorithms, MTI, MTD, PD-related simulation.)

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.)