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使用遗传算法求函数的极小值的一个例子，使用遗传算法求函数的极小值的一个例子(the use of genetic algorithms to find the minimum value function of an example, the use of genetic algorithms to find the minimum value function of an example)

说明：  基于matlab运用最小二乘法对信号进行消除趋势项处理以及五点三次平滑法对信号进行平滑处理，消除毛刺等(denoising vibration signal based on matlab)

蚁群算法的matlab实现源码，具有很大的参考价值(Ant colony matlab implementation source of great reference value)

自己编写的程序，在光谱数据处理上可以实现截回波以及傅里叶变换功能(I have written procedures, the spectral data processing can be achieved as well as truncated Fourier transform function echo)

时间序列中，ARIMA（1,1,1）模型实例，通过计算得到这一过程的十步预测(Time series, (1,1,1) model instance ARIMA, obtained by calculating this process ten-step prediction)

二维经验模态分解，采用形态学求极值点，径向基神经网络拟合，速度较快。(Two-dimensional empirical mode decomposition, the use of morphology for extreme points, Radial Basis Function Neural Network Fitting, faster.)

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)

随机优化算法的代码，你可以在此基础上修改解决你自己的问题(stochastic_opt)

一种谱估计的算法，大家可以借鉴下，并且共同讨论下，是matlab编写的(a spectral estimation algorithm, we can draw on, and discuss, it is prepared by the Matlab)

求解常微分方程的龙格-库塔（Runge - Kutta）法(Ordinary differential equations RungeKutta method)