gravity-field

基于人工神经网络的手写英文字母、数字图像识别的源程序代码。(Based on Artificial Neural Network handwritten letters of the alphabet, the digital image to identify the source code.)

wimax receiver description

模拟激光光束在大气中传输,分析激光强度,光斑质量(The analog laser beam transmission, analyzing the laser light intensity, spot quality in the atmosphere)

虚拟力的无线传感网络覆盖，MIMO OFDM matlab仿真,实现了对10个数字音的识别程序欢迎大家下载学习，使用高阶累积量对MPSK信号进行调制识别，车牌识别定位程序的部分功能，相关分析过程的matlab方法。( Virtual power wireless sensor network coverage, MIMO OFDM matlab simulation, Realization of 10 digital audio recognition program Welcome to download the study, Using high-order cumulants of MPSK signal modulation recognition, Part of the license plate recognition locator feature, Correlation analysis process matlab method.)

现在数字信号处理相关，本章主要讲述自适应滤波器(Modern digital signal processing)

simple implementation of a morse code project

一种子空间跟踪PASTd经典算法MATLAB程序(A classic PASTd subspace tracking algorithm for MATLAB program)

快速傅里叶变换例子 matlab code

matlab 简介滤波程序代码，不错的哦，分享给大家看看(matlab lvbochengxu，hahhahahhhhhhhhh)

说明：  多目标遗传算法是NSGA-II[1]（改进的非支配排序算法）,该遗传算法相比于其它的多目标遗传算法有如下优点：传统的非支配排序算法的复杂度为 ，而NSGA-II的复杂度为 ，其中M为目标函数的个数，N为种群中的个体数。引进精英策略，保证某些优良的种群个体在进化过程中不会被丢弃，从而提高了优化结果的精度。采用拥挤度和拥挤度比较算子，不但克服了NSGA中需要人为指定共享参数的缺陷，而且将其作为种群中个体间的比较标准，使得准Pareto域中的个体能均匀地扩展到整个Pareto域，保证了种群的多样性。（消除了共享参数）。(Multi-objective genetic algorithm is nsga-ii [1] (improved non-dominant sorting algorithm), which has the following advantages compared with other multi-objective genetic algorithms: the complexity of the traditional non-dominant sorting algorithm is, while the complexity of nsga-ii is, where M is the number of objective functions and N is the number of individuals in the population.The introduction of elite strategy to ensure that some good individuals in the evolutionary process will not be discarded, thus improving the accuracy of the optimization results.The comparison operator of crowding degree and crowding degree not only overcomes the defect that NSGA needs to specify the Shared parameter artificially, but also takes it as the comparison standard between individuals in the population, so that individuals in the quasi-pareto domain can uniformly expand to the whole Pareto domain, ensuring the diversity of the population.(eliminating Shared parameters).)