10GNRZ_optimization

该实例主要功能： 1、演示了GUI界面的布局和效果； 2、演示了togglebutton、pushbutton对simulink模块参数的设置方法； 3、演示了Edit text 和slider的联合使用，及利用edit text 和slider 对simulink 参数的动态修改； 4、演示了simulink参数在GUI上的显示和更显； 5、语句进行了注释，为便于编译没用中文，大家凑或者看吧！(The main functions of examples: 1, presentation of the layout of the GUI interface and effectiveness 2, demonstrated togglebutton, pushbutton module to simulink Ways parameters 3, Edit text and to demonstrate the joint use of slider, and the use of the slider and edit text simulink dynamic parameters modified 4, demonstrated on simulink parameter GUI at the display and more 5, statement to the Notes, in order to facilitate the compiler no English, or U.S. Check it out together!)

基于matlab的小波分析程序，可以广泛应用于各行业，主要用于分析小波周期、频率等(Wavelet analysis based on matlab procedures, can be widely used in various industries, mainly for wavelet analysis period, frequency, etc.)

This is about viterbi algorithm

说明：  speech recognition using matlab

the submitted document is a series of files haaving codes for ber curves

winner滤波器matlab代码实现，已测试(winner filter matlab code, has been tested)

人工鱼群算法是李晓磊等人于2002年在动物群体智能行为研究的基础上提出的一种新型方盛优化算法，该算法根据水域中鱼生存数目最多的地方就是本水域中富含营养物质最多的地方这一特点来模拟鱼群的觅食行为而实现寻优。算法主要利用鱼的三大基本行为：觅食、聚群和追尾行为，采用自上而下的寻优模式从构造个体的底层行为开始，通过鱼群中各个体的局部寻优，达到全局最优值在群体中凸显出来的目的。 这是一个鱼群算法的案例，希望大家可以有所收获(Artificial fish swarm algorithm is a new fangsheng optimization algorithm proposed by li xiaolei et al. on the basis of the research on intelligent behavior of animals in 2002. This algorithm simulates the foraging behavior of fish swarm according to the characteristics that the place with the largest number of fish survival is the place with the largest number of nutrients in the water area.)

这是TD算法的逆过程，首先将时间序列，划分为相邻点的短序列，当然此时的拟合误差为0，因为第一点和第二点的连线，原始点都落在线段上。将相邻两个线段连接起来，此时每条线段包含三个原始点，计算中间那个点的拟合误差。这样，所有这些三个点的线段中的中间点的拟合误差计算出来后，找出误差最小且误差小于阀值R的分段，作为第一条包含三个点的线段。 在上面的基础上，第一条分段同样的和相邻线段连接，然后计算每一条分段的拟合误差，再找出误差最小且小于阀值R的分段，作为第二个分段。依次方式循环，直到所有分段的拟合误差都小于阀值R，分段结束。 (This is the reverse process of TD algorithm, the first time series is divided into short sequences adjacent dots, of course fitting error is zero at this time, because the first and second points of connection, the original point fall line on. The two adjacent segments are connected, then each segment contains three original point, the intermediate fitting error calculated that point. Thus, after all these segments fitting error of three points in the intermediate point calculated to find the minimum error and the error is less than the threshold R segment, as the first line contains three points. Based on the above, the first segment and the adjacent line segment connecting the same, and then calculate a fitting error for each segment, and then find the smallest error and less than the threshold R segment, as the second segment . Followed by a cyclic manner until all segments fitting error is less than the threshold R, segment ends.)

Motion estimation algorithm - New three step search

本书主要讲解了MATLAB语言常用算法程序集清晰版，对于初学者用处很大(This book explains the MATLAB language commonly used algorithm for assembly clear version, has proved very useful for beginners)