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三种求矩阵特征值的matlab实现：乘幂法、QR算法、yaccbi法求特征值(Eigenvalues of the three Matrix matlab implementation: power method, QR algorithm, yaccbi Eigenvalue Method)

说明：  MATLAB神经网络30个案例分析相应matlab数据和源代码(MATLAB neural network analysis of 30 cases, the data and the corresponding source code matlab)

逆合成孔径雷达一些基本原理的代码，适合初学者使用(Inverse synthetic aperture radar some basic principles of the code, suitable for beginners)

包含多种噪声谱估计算法用于语音增强，采用的是标准谱减法，主要用于对各种噪声谱估计算法对比，详见readme.txt(Ppls spectral subtraction noise spectrum estimation, mainly used to compare the performance of different noise spectrum estimation algorithms, including Martin s minimum tracking algorithm, Minimum controlled recursive average algorithm (Cohen), variant of Minimum controlled recursive average algorithm, improved Minimum controlled recursive average algorithm (Cohen ), continuous spectral minimum tracking (Doblinger), weighted spectral average (Hirsch & Ehrilcher), connected frequency regions (Sorensen & Andersen). See readme.txt inside)

本Matlab代码是检验一个有向图是否是一个弱距离正则图(The Matlab code is to test a directed graph is a weak distance-regular graph)

该文件含有高斯信号产生的完整代码，用matlab编写的，并含有详细的注释,另外还有两个小波基的使用的详细注解(This file contains the complete code for the Gaussian signal is generated, using matlab, and contains detailed notes, in addition to the use of two small Porgy detailed comments)

模式识别最大最小距离算法实现，matlab实现的，可以直接运行(Pattern recognition of maximum and minimum distance algorithm to achieve the Matlab)

problems of optical communications

说明：  一个用Visual C语言编写的PID控制程序，在MATLAB上运行实现。(A language with Visual C of PID control procedures, running in MATLAB to achieve.)

说明：  遗传算法（Genetic Algorithm）是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法，它最初由美国Michigan大学J.Holland教授于1975年首先提出来的，并出版了颇有影响的专著《Adaptation in Natural and Artificial Systems》，GA这个名称才逐渐为人所知，J.Holland教授所提出的GA通常为简单遗传算法（SGA）。(a genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.)