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几个非线性系统学习的源码，包括李雅普诺夫指数，洛伦兹吸引子的画法，boost开关变换器，分叉图等等，适合非线性系统的学习仿真(Several of the source study of nonlinear systems, including Lyapunov exponents, Lorenz attractor painting, boost switching converter, bifurcation map, etc., suitable for simulation study of nonlinear systems)

矩形窗、汉宁窗的函数源代码，用于查看其时域和频域波形；求过零率的函数以及其源代码(Rectangular window, Hanning window function of the source code for the view of its time-domain and frequency-domain waveform seeking function of the rate of zero, and its source code)

说明：  该文件为神经网络预测的源代码，读入数据后进行训练，然后可进行数据预测。(This file is the prediction of neural network source code, read data after training, and data prediction.)

神经网络函数逼近源代码,函数用了sin三角函数，你也可以随便换成其他函数使用(Neural network function approximation source code, with the sin trigonometric functions, you can easily switch to other functions use)

蚁群算法程序，我用过的人，根据具体情况，修改一些参数就可得结果(Ant colony algorithm program, I used people, depending on the circumstances, the results obtained can modify some parameters)

说明：  BUCK-BOOST电路，带有PI控制回路。(BUCK-BOOST circuit with PI control circuit)

这个一个进行频率检测的程序，简单实用，欢迎大家下载学习(The frequency of detection of an ongoing program, simple and practical, are welcome to download the study)

c均值聚类算法小论文，包括原理、实验及结论(papers, including c-means clustering algorithm principle experiments and conclusions)

盲信号分离的源信号模型，要更好的了解和利用时间序列的内在时间结构和复杂特性，通常可以采用相应的数学模型区近似描述各种类型的数据，该程序采用泛化自回归模型来产生盲源信号(Blind signal separation of the source signal model, we must better understand and leverage the structure of time series Neizai He Fuzatexing time can generally be adopted similar to the corresponding mathematical model Ou Miaoshu type of data, Gai Chengxu using generalized regression models to blind signal generated)

Matlab中没有现成的Lagrange插值函数，必须编写一个M文件实现Lagrange插值。 设n个节点数据以数组x0,y0 输入（注意Matlat的数组下标从1开始），m 个插值点以数组 x输入，输出数组y 为 m个插值。 编写一个名为lagrange.m的M文件。(Matlab is not off-the-shelf Lagrange interpolation function, we must realize the preparation of an M file Lagrange interpolation. N nodes based data to an array of x0, y0 input (Note Matlat subscript of the array starting at 1), m a x interpolation points to the array input and output array y for m months interpolation. Lagrange.m the preparation of a M-file.)