Code-for-bign

本程序适用于自适应波束形成的几种算法的matlab仿真。(This procedure applies to adaptive beamforming algorithms for the matlab simulation.)

有matlab各种实用程序300多个，涵盖信号及图像处理，优化，各种科学计算，简明易学(Matlab utilities have a variety of more than 300, covering the signal and image processing, optimization, scientific computing, concise easy to learn)

说明：  包含了《精通MATLAB》书中所有源代码，极具参考与实用价值！(Contains the )

This is the code for kalman fiter, smoothing used in state space model

采用MFCC算法提取出能表征信号本质特征信号的参数，进一步对信号进行分类研究作准备(describle the MFCC feturae)

针对基本无线传感器网络DV-Hop定位算法中，计算平均跳距时产生偏小误差的原因，提出了基于平均跳距修正的DV-Hop算法。改进算法中，引入信标节点个数比例、所有信标节点平均跳距的最大值以及所有信标节点平均跳距的平均值三个因子，并采用它们对平均跳距进行修正。通过Matlab软件对算法进行仿真验证，结果表明，改进后的 DV-Hop 算法有效降低了未知节点的定位误差，是一种可行的无线传感器网络节点定位解决方案。(DV-Hop localization algorithm for wireless sensor networks, we calculate the average hop distance small error of reasons, raised the DV-Hop algorithm based on the average hop distance correction. Improved algorithm, the proportion of the number of nodes, the introduction of beacons, the beacon node average maximum jump distance as well as all the beacon nodes average jump from the average of three factors, and their average hop distance correction. Matlab software simulation results show that the improved DV-Hop algorithm effectively reduce the unknown node positioning error, the solution is a viable wireless sensor network node positioning algorithm.)

基于蚁群算法的路径规划，栅格法，双向路线寻优，包含栅格程序，能运行(Ant colony algorithm based path planning, grid method, two-way route optimization, including raster program, can run)

以matlab撰写的遗传算法，可以解决工程问题的优化。验证功能良好。(Matlab code for genetic algorithms. )

摘　要:提出一种新的基于Pareto多目标进化免疫算法(PMEIA)。算法在每一代进化群体中选取最优非支配抗体保存到记忆细胞文档中 同时引入Parzen窗估计法计算记忆细胞的熵值,根据熵值对记忆细胞文档进行动更新,使算法向着理想Pareto最优边界搜索。此外,算法基于点在目标空间分况进行克隆选择,有利于得到分布较广的Pareto最优边界,且加快了收敛速度。与已有算法相比, PMEIA在收敛性、多样性,以及解的分布性方面都得到很好的提高。(Abstract:This paperproposed a new pareto-based multi-object evolutionary mi mune algorithm(PMEIA). PMEIA selected optmi alnon-dominated antibodieswhichwere then reserved inmemory cellarchive, and introducedParzenwindow to calculate entropy ofmemory cells. Updated thememory cell archive according to entropy ofmemory cells. This guarantees the conver- gence to the true Pareto fron.t Moreover, the performance of clone selection was dependent on distribution in the objective space, whichwas favorable forgetting awidely spreadPareto frontand mi proving convergence speed. Comparedwith the exis- ted algorithms, the obtained solutions ofPMEIA havemuch betterperformance in the convergence, diversity and distribution.)

用于matlab中图像后处理，将图像灰度化，二值化(For processing the image in MATLAB, the gray image, the value of the two)