daolibai

说明：  使用抖动创建图像。使用抖动得到的图像看上去有更多的颜色数；不适用抖动得到的图像看上去颜色数少，大门空间分辨率要高些。(Creating images using dithering. Jitter by using the image appears to have more number of colors not apply to jitter resulting image looks small number of colors, the door to higher spatial resolution.)

digital signal processing

简单程序，大家可以来参考参考，希望能有所帮助(Simple procedure, everyone can come to refer to the reference, hoping to be helpful)

模拟OFDM体制的数字电视信号DVB-T的接收全过程，包括下变频、D/A转换、滤波、星座解调等。。。很实用！(Simulated OFDM system, DVB-T digital TV signal reception of the entire process, including down-conversion, D/A conversion, filtering, demodulation and other constellations. . . It works!)

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)

lms program for matlab

改进前推回代潮流算法，计算潮流，适应强，迭代次数少。(Improved push back the tide before generation algorithms, computing trends, adaptable, less iterations.)

The real-coded genetic algorithm to run SR mechenical.

说明：  设{ ,k=0,1,2,…}为N（0，1）白序列， ， =0，1,2,…,1000 。求： ； (Let {k = 0,1,2, ...} white sequence of N (0,1) = 0,1,2, ..., 1000 Requirements: )

An important aspect of an ANN model is whether it needs guidance in learning or not. Based on the way they learn, all artificial neural networks can be divided into two learning categories - supervised and unsupervised. • In supervised learning, a desired output result for each input vector is required when the network is trained. An ANN of the supervised learning type, such as the multi-layer perceptron, uses the target result to guide the formation of the neural parameters. It is thus possible to make the neural network learn the behavior of the process under study. • In unsupervised learning, the training of the network is entirely data-driven and no target results for the input data vectors are provided. An ANN of the unsupervised learning type, such as the self-organizing map, can be used for clustering the input data and find features inherent to the problem.