QAM

This is matlab/simulink code to develop frequency domain conversion

说明：  潮流计算的程序，于5ieee,14ieee,30ieee都运行的不错，对于节点较多的没有验证(Flow calculation procedure, in 5ieee, 14ieee, 30ieee are running well, no more for the node to verify)

PCA to face Recognition

This is a matlab software package for butterworth band pass filter design. you could change the high pass and low pass frequency to design the betterworth filter.

MATLAB环境，利用基于径向基函数的神经网络实现曲线拟合。(MATLAB environment, the use of neural network based on radial basis functions and curve fitting.)

人工蚁群算法，提供了一种高效的编程步骤。(Artificial ant colony algorithm, provides an efficient programming steps.)

说明：  在求解装备的资源约束型装配线一类平衡问题时，借助遗传算法和粒子群算法，并对遗传算法和粒子群算法进行改进，利用matlab程序进行优化求解。(The type 1 assembling line balancing problem for certain equipment is sovled with modified genetic-particle swarm optimization algorithm by using matlab languge.)

用局部参数最优化方法设计一个模型参考自适应系统,可调增益的初值Kc(0)=0.2，给定值r(t)为单位阶跃信号，即r(t)=A×1(t)。 要求： 1把连续系统离散化(采样时间可取0.1)。 2编制并运行这个系统的计算机程序(注意调整B值，使系统获得较好的自适应特性)。 3记录ym、yp的曲线 记录kp×kc的曲线 记录广义输出误差e的变化曲线。 4在参数收敛后，让Kp=2变为Kp=1，重新观察Kp×Kc及e的变化曲线。 5找出在确定的B值下，使系统不稳定的A值(阶跃信号的幅值)，并与用劳斯稳定判据计算的结果比较。 (With local parameter optimization method to design a model reference adaptive system, adjustable gain initial Kc (0) = 0.2, for a given value of r (t) for the unit step signal that r (t) = A × 1 ( t). Requirements: a continuous system discretization (sampling time of 0.1 preferred). 2 compiled and run the system computer program (Note B to adjust the value of the system to obtain better adaptive characteristics). 3 records ym, yp curve record kp × kc curve records generalized output error e curves. 4 parameter convergence, let Kp = 2 into the Kp = 1, re-observed Kp × Kc and e curves. B-5 to find value in determining the next, making the system unstable A value (step signal of amplitude), and using Routh stability criterion and the comparison of the results calculated.)

对UCI数据集之一进行PCA特征抽取实验,给出在二维PCA特征空间的数据散点图。(UCI data sets on one of PCA feature extraction experiments are given in the two-dimensional PCA feature space of the data scatter.)

说明：  利用matab和vc混合编程的简单小例子，学习的同学可以参考一下(Hybrid programming using vc matab and simple small example, students can refer to learning)