DVB_T_tongbu_1

TP simulation of inverter

电池全寿命加速老化试验的采集数据。在不同温度下的充电和放电下完成。数据格式为matlab的mat，文件里面有详细的实验数据说明，希望对研究电池寿命预测和诊断的同志有帮助。(Battery life-accelerated aging test data collection. At different temperatures to complete the charging and discharging. The data format is the matlab mat, which has detailed documentation of the experimental data shows that the hope of battery life prediction and diagnosis to help comrades.)

遗传算法源代码，主要用于车间调度的优化算法(Genetic algorithm source code, mainly for shop scheduling optimization algorithm)

强预测器算法进行时间序列的预测，使得效果更好(Strong predictor algorithm)

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)

本书提供一些matlab程序代码,能为初学者提供很好的例子(This book provide lots of code of matlab and good example for beginners.)

小区初搜为GSM系统中的一个关键过程，本程序为GSM小区初搜过程的第一部，查找频率校正突发的matlab程序，输入数据由采样数据得到，在转换成所需格式后进行处理。若其中包含fcch,则输出找到信息。若没有，则查找下一个突发。内付算法详细说明文档()

基于一维自主神经网络的图像融合，lena图像融合(One-dimensional autonomous neural network-based image fusion, lena image fusion)

小波变换的matlab代码，用于信号处理(Wavelet transform matlab code for signal processing)

说明：  更新部分 1、自适应算法采用参考文献[2]的NLMS算法，收敛速度更快，所需训练次数更少。 2、自适应收敛步长在(0,2)之间存在较优取值，Lorenz序列为0.6。(update an adaptive algorithm reference [2] NLMS algorithm, convergence faster, less the number of required training. 2, adaptive step in the convergence of (0,2) between the optimum value, Lorenz sequence of 0.6.)