matlab.1

不适用任何库函数，自己用matlab编写的直方图均衡化，可以看到结果图片(realize the Histogram Equalization without extant function)

auto correlation a shift a bit sequence in matlab

Dijkstra algorithm implemented by c++

说明：  给定n种物品和一个背包,物品i的重量是Wi,价值是vi,被包容量是C,应该如何选择装入被包的物品,使得装入背包中物品的总价值最大?对于每种物品i,只有两种选择,装入或者不装入. 输入:第一行两个正整数n(物品数)和C(容量) 第二行n个正整数n,表示物品价值 第三行n个正整数n,表示物品重量 输出:第一行表示总价值 第二行n个数(0/1),0表示物品不装入,1表示装入 例如, 输入: 5 10 6 3 5 4 6 2 2 6 5 4 输出 15 1 1 0 0 1 (Species of a given n items and a backpack, the weight of item i is Wi, the value of vi, was packet capacity is C, should be how to choose a package of items to be loaded, the backpack load in the largest total value of goods? For each item i, there are only two options, loaded or not loaded. Input: the first line of the two positive integer n (the number of items) and C (capacity) of the second line of n positive integer n, that the value of the third line of n positive integer n, that the weight of output: the total value of the first line of the second line, said n number of (0/1), 0 that the items do not load, that load 1 for example, enter: 5,106,354,622 6 5 4 output 15 1 1 0 0 1)

说明：  BP神经网络的设计实例（例子均来自于互联网，本人的工作只是将多个例子整合在一起）(BP neural network design example (examples are from the Internet, my work is integrated with multiple examples))

matlab 入门教程 适合刚入门的学生学习(matlab Tutorial)

IMC-PID是利用内模控制原理来对PID参数进行计算，毕业设计有用，采用加权网络中节点强度和权重都是幂率分布的模型，加入重复控制，采用累计贡献率的方法，实现了对10个数字音的识别，借鉴了主成分分析算法（PCA），双向PCS控制仿真。( The IMC- PID is using the internal model control principle for PID parameters is calculated, Graduation useful Using weighted model nodes in the network strength and weight are power law distribution, Join repetitive control, The method of cumulative contribution rate To achieve the recognition of 10 digital sound, It draws on principal component analysis algorithm (PCA), Two-way PCS control simulation.)

DC Motor Thermal Circuit The demo illustrates how the thermal behavior of a motor can be simulated in lumped parameters.

dijstra algorithm..this is the trial version i tried to implement it on matlab

UFLDL作业，以下作业均已完整做好，build deep network,vectorization,softmax,working with large images,pca。(UFLDL,build deep network,vectorization,softmax,working with large images,pca)