SimulinkDFIGMOXING

关于CPC的程序，自己编的，希望大家喜欢(On the CPC program, its compilation, and I hope everyone likes)

tutorial of OpenCV and Matlab

matlab7.0一书的电子教案，PPT文档，以及书中的程序，不错的资源(matlab7.0 book electronic templates, PPT files, and the program book, a good resource)

针对风电场短期风速的预测提出一种基于小波变换的组合预测方法。首先利用Mallat 算法对短期风速时间序列进行db3 小波三层分解与重构，得到短期风速时间序列的近似分量和细节分量。针对近似分量和细节分量的不同特性，对近似分量利用粒子群算法优化的最小二乘支持向量机进行预测，对细节分量利用自回归求和滑动平均模型进行预测。最后各预测模型预测值组合叠加得到最终的短期风速预测值。仿真结果表明该方法具有较高的预测准确度。(In order to improve short-term wind speed prediction accuracy of wind farms，a combined prediction method based on the wavelet transform is proposed. Firstly，the db3 wavelet is used for three-layer decomposition and reconstruction for short-term wind speed time series through Mallat algorithm. The approximation components and the detail components of the short-term wind speed are then obtained. Next，according to the different characteristics of these components，the least square support vector machine optimized by particle swarm algorithm and the autoregressive integrated moving average model are adopted as the predictivemodels for the approximate components and the detail components respectively. Then，the final predictive value of the short-term wind speed is obtained by the combination of the two components. The simulation results indicate that higher accuracy can be obtained in this prediction method.)

基于谱减法的语音增强算法，谱相减法及其变形方法总体上运算量较小，容易实时实现，增强效果也较好，是目前最常用的一种方法(Spectral subtraction based speech enhancement algorithm, spectral subtraction method and the deformation of the whole volume of the smaller operations, easy real-time implementation, and enhance the results would be better, is the most commonly used in a way)

材料力学上机的弯曲梁程序（GUIDE），可以作为参考(Mechanics of materials bending beam program (GUIDE))

说明：  matlab中很实用的源代码1，例图图形转换、非线性变换等等(Practical source code of MATLAB)

说明：  MATlab和GA的潜用，将GA的算法程序写到MATLAB里面，用于求解优化问题(MATlab and GA' s potential use, the GA algorithm program written MATLAB inside, for solving optimization problems)

shortest path algorithms

he basic idea of the method of bisection is to start with an initial interval, [a0,b0], that is chosen so that f(a0)f(b0) < 0. (This guarantees that there is at least one root of the function f(x) within the initial interval.) We then iteratively bisect the interval, generating a sequence of intervals [ak,bk] that is guaranteed to converge to a solution to f(x) = 0.