Dispersion2

circular convolution

数字识别的matlab仿真，通过图像裁剪，再有神经网络训练，可以达到很好的效果 (Digital Identification of matlab simulation, cutting through the images, there was a further neural network training, can achieve good results)

　k-means 算法接受参数 k ；然后将事先输入的n个数据对象划分为 k个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个“中心对象”（引力中心）来进行计算的。(K-means algorithm accept parameter k Then will the n of prior input data object is divided into k clustering to make won clustering meet: the same clustering the object in the similarity is higher And different clustering the object in the smaller similarity. Clustering similarity is using the cluster of the object in the mean gained a "center object" (the center of gravity) to calculate. )

【matlab编程代做】使用两个图像输入模糊C+-均值图像分割 可以做为参考使用学习([Do] matlab program generation using two image input fuzzy C+- mean value can be used as a reference image segmentation using learning)

These are knn algorithm documentations...

power flow by newton-aphson method

在进行交通流仿真时考虑两类车，不同的车长和不同的最大速度。(Consider when making traffic flow simulation two cars, a different car lengths and different maximum speeds.)

802.11a通信协议仿真matlab程序，其中有些子函数已经有大神上传过了。(802.11a protocol simulation matlab procedures, some of which subroutine has uploaded a great God)

说明：  使用粒子群算法对无线传感器网络进行聚类（PSO）(Particle swarm optimization (PSO) is used to cluster wireless sensor networks)

任务： 1） 借助MATLAB画出误差性能曲面和误差性能曲面的等值曲线（参考PPT2.1第17页的两幅图）； 2） 写出最陡下降法以及LMS算法的计算公式（取 ）； 3） 用MATLAB产生方差为0.05, 均值为0白噪音S(n)，并画出某次采样得到的波形（即产生任意一个噪声随机序列）； 4） 根据 2）中的公式，并利用 3）中产生的S(n)，在 1）中的误差性能曲面的等值曲线上叠加画出采用最陡下降法以及LMS法时H(n)的在叠代过程中的轨迹曲线（参考PPT2.1第17页的右下图的曲线1和曲线2）。 5）用MATLAB计算并画出LMS法时 随时间n的变化曲线（对应S(n)的某一次的一次实现）和e(n)波形； 注意：某一次实现的结果并不能从统计的角度反映实验的结果 的正确性，为得到具有统计特性的实验结果，可用足够多次的 实验结果的平均值作为实验的结果。用MATLAB计算并画出 采用LMS法时，J(n)的100次实验结果的平均值随时间n的变 化曲线（即 生成随机噪声信号并计算结果，重复执行100次， 求平均结果）。 6）在 1）中的误差性能曲面的等值曲线上，叠加画出采用LMS法得到的100次实验中的H(n)的平均值的轨迹曲线； (Task: 1 ) Draw with MATLAB error performance surface and surface contour error performance curve ( refer PPT2.1 two chart on page 17 ) 2 ) Write the steepest descent method and the LMS algorithm formula ( take ) 3 ) Using MATLAB to generate variance of 0.05 with a mean of 0 white noise S (n), and draw a particular sampling waveform ( ie, any noise generated random sequence ) 4 ) based on 2 ) of the formula , and use 3 ) generated in S (n), in a ) the error performance curves superimposed on the surface contour plot using the steepest descent method and the LMS method when H (n) the iterative process in the trajectory curve ( refer PPT2.1 page 17 bottom right of the curves 1 and 2 ) . 5 ) using MATLAB LMS method is used calculate and draw the curve with time n (corresponding to S (n) of a first implementation of a ) and e (n) waveform Note : in a first implementation of the results are not reflected from a statistical point of view of accuracy of the experimental results ,)