theNewtonlaw

遗传算法是模拟达尔文的遗传选择和自然淘汰的生物进化过程的计算模型。它的思想源于生物遗传学和适者生存的自然规律，是具有“生存＋检测”的迭代过程的搜索算法。遗传算法以一种群体中的所有个体为对象，并利用随机化技术指导对一个被编码的参数空间进行高效搜索。(Genetic algorithm is simulated Darwinian natural selection of genetic selection and evolution process model. It' s thought from biological genetics and survival of the fittest laws of nature, is a " survival+ detection" iterative process of search algorithm. Genetic algorithms in a group all the individuals targeted, and technical guidance on the use of a randomized coded efficiently search parameter space.)

基于matlab软件下的IHS影像融合算法，是一种常规的技术(IHS image fusion algorithm)

该编程为矩阵与数据分析课的编程作业程序，里面包括matlab的三次样条曲线程序，有Jacobi迭代法和Gauss-Seidel迭代法等(The programming and data analysis for the matrix class programming operations program, which includes the cubic spline curve matlab procedures, Jacobi and Gauss-Seidel iterative method iterative method)

GA for flow shop,include GA and SMGA,need ga toolbox(GA and SMGA)

dojo基类扩展的三种方法说明，包括extend，declare和mixin等。(dojo base class extended three methods described, including extend, declare and mixin like.)

说明：  这是用MATLAB编写的规划问题的程序，用的着的请参考。(This is prepared by using MATLAB programming procedures, please use the reference.)

利用逆转换法，用均匀分布随机数产生指数随机数的数学实验，并绘制直方图检验效果,有兴趣的matlab初学者不妨看看，直方图绘制方法可推广借鉴(mathematical experiment: using a random number to create exp distributed random index number, and drawing the histogram test the effect, catering to interested matlab beginner.)

对电力系统中传统机组进行机组组合优化计算(The unit commitment optimization of the traditional power unit is calculated)

There are two inputs to the fuzzy controller: the water temperature and the flow rate. The controller uses these inputs to set the position of the hot and cold valves

EM 算法是Dempster，Laind，Rubin于1977年提出的求参数极大似然估计的一种方法，它可以从非完整数据集中对参数进行 MLE估计，是一种非常简单实用的学习算法。这种方法可以广泛地应用于处理缺损数据、截尾数据以及带有噪声等所谓的不完全数据，可以具体来说，我们可以利用EM算法来填充样本中的缺失数据、发现隐藏变量的值、估计HMM中的参数、估计有限混合分布中的参数以及可以进行无监督聚类等等。(EM algorithm is a method for maximum likelihood estimation of parameters proposed by Dempster, Laind and Rubin in 1977. It can estimate parameters from incomplete data sets by MLE. It is a very simple and practical learning algorithm. This method can be widely used in dealing with the so-called incomplete data such as defective data, censored data and noisy data. Specifically, we can use EM algorithm to fill missing data in samples, find hidden variable values, estimate parameters in HMM, estimate parameters in finite mixed distribution, and so on. Unsupervised clustering and so on.)