DeepLearning Toolbox

窗口移动多项式平滑演示程序。。。二维数据模拟程序(Polynomial smoothing window mobile demo program. . . Two-dimensional data simulation program)

用于函数优化问题的混合粒子群文化算法源程序(program of function optimization based on Cultural algorithm )

matlab基于灰色理论的去除粗大误差的代码(remove coarse error matlab code based on gray theory)

反射式血氧（SPO2）计算，包括基线滤波，脉搏波特征点提取，R值计算，可移植到单片机（STM32）(Reflective oximetry (SPO2) calculations, including baseline filtering, pulse wave feature point extraction, R value calculation, can be transplanted to the microcontroller (STM32))

说明：  Matlab实现BP神经网络进行数据预测(Matlab realizes BP neural network for data prediction)

包括最后计算压缩图像的峰值信噪比和压缩效果的源码,信号维数的估计，可以实现模式识别领域的数据的分类及回归，有小波分析的盲信号处理，采用热核构造权重，光纤无线通信系统中传输性能的研究。

用matlab实现了3次样条，拉格朗日。辛普生、梯形等数值分析里面的重要内容。代码可用性强，可以直接调用。另外还有实验报告，非常详细的报告。(A 3 times with matlab spline, Lagrange. Simpson, trapezoidal numerical analysis and other important content inside. Code availability strong, can be called directly. There is also experimental report, very detailed report.)

liner fuzzy inverted pendulum

人脸识别中特征提取matlab源码，LDA，(Face Recognition feature extraction Matlab source, LDA.)

DTW算法的程序，申请两个n*m的矩阵D、d,分别为累积距离和帧匹配距离。这里n和m为测试模版与参考模版的帧数。然后通过一个循环计算两个模版的帧匹配距离d。接下来进行动态规划，为每个格点 （i，j）都计算其三个可能的前续格点的累积距离D1，D2，D3。考虑到边界问题，有些前续格点可能不存在，因此加入一些判断条件最后利用最小值函数min()，找到三个前续格点的累积 距离作为累积距离，与当前帧的匹配距离d（i，j）相加，作为当前格点的累积距离。该计算过程一直达到格点（n，m），并将D（n，m）输出，作为模版匹配的结果。(DTW algorithm procedures, apply two n* m matrix D, d, respectively, accumulated distance and frame matching distance. Where n and m is a reference test pattern and the template frames. Then through a loop to calculate two frames template matching distance d. Next, dynamic programming, for each grid point (i, j) are calculated from the cumulative D1 before the renewal of its three possible lattice point, D2, D3. Taking into account the boundary problem, some former continued grid point may not exist, so add some conditions to determine the final use of the minimum function min (), found three former continued accumulation of grid points cumulative distance as the distance from the current frame to match the d (i, j) added, and a current cumulative distance grid. This calculation has reached the grid (n, m), and D (n, m) output, as a result of template matching.)