阴影去除前景检测在视频监控中的一种鲁棒算法

采用一种快速收敛变步长LMS（Least mean square ） 自适应最小均方算法matlab源程序，其中算法所做的工作是用FIR 滤波器的预测系统，对IIR系统进行预测，如果阶数越高越能逼近被预测系统。(Using a fast convergence of variable step size LMS (Least mean square) adaptive least mean square algorithm matlab source, one of algorithm is the work done by FIR prediction filter systems, prediction of IIR systems, if the higher order the more it can be prediction system approximation.)

madlab c++ 接口，Madlab的一本很好的书(madlab c++ interface, Madlab)

ecg matlab source code for medical application

里面包含了曲线演化的两种形式，常量演化和曲率演化。(Which contains the curve evolution of two forms, the constant evolution and the evolution of curvature.)

多变量多输入输出系统的鲁棒控制, 电子版，学习材料，欢迎下载(MIMO system robust control)

协同区间偏最小二乘 siPLS算法是 N rgaard等对其提出 的 iPLS方法的改进, 其基本算法步骤如下:(1)对原始谱图进行 预处理;(2)在全谱范围内建立全局偏最小二乘模型, 即上节的 模型;(3)在整个光谱区间采用 iPLS建立多个等窗口宽度的子 区间, 假设为 n个;(4)在每个子区间上建立偏最小二乘法模型, 即可得到 n个局部模型;(5)以交叉验证时的均方根误差 RMSE 值为各模型的精度衡量标准, 比较全光谱模型和各局部模型的 精度;(6)组合精度最高的局部子区间为新的回归区间;再在新 的回归区间建立 siPLS模型(Cooperative interval partial least squares (siPLS) algorithm is proposed by N rgaard et al. The improvements of the iPLS method are as follows: (1) the original spectrogram is performed. Preprocessing; (2) establishing a global partial least squares model in the full spectrum, that is, the upper part of the model. Model; (3) use iPLS to establish multiple equal window widths in the whole spectral interval. The interval is assumed to be n; (4) a partial least squares model is established on each sub interval. N local models can be obtained; (5) the root mean square error of cross validation is RMSE The values are the accuracy criteria of each model, and the comparison between the full spectral model and the local models is made. (6) the local interval with the highest combined precision is a new regression interval; and then new. The establishment of siPLS model in the regression interval)

ask、psk、fsk等的产生波形的matlab程序，还不错()

说明：  产生等边三角形待定节点的matlab程序(Have to be an equilateral triangle matlab process node)

说明：  一个简单的图像的kl变换matlab程序,这里需要自己找图像，不如经典的lina图像(a simple image kl transform Matlab procedures here need to find your own image, as the classic images Lina)

通过对集成误差公式的理论分析，提出了一种能主动引导个体网络进行差异性学习的集成网络学习算法。该方法通过对集成误差的分解，使个体网络的训练准则函数中包含个体网络误差相关度的因素，并通过协同训练，引导个体网络进行差异性学习。该方法在基于油气分析的变压器故障诊断的实验结果表明，该方法的故障诊断准确率优于传统的三比值法与BP神经网络，其性能也比经典的集成方法Bagging和Boosting方法更稳定可靠。(A learning algorithm is proposed in this paper by analyzing the error function of neural network ensembles, in which individual neural networks are actively guided to learn diversity. By decomposing the ensemble error function, error correlation terms are included in the learning criterion function of individual networks. And all the individual networks in the ensemble are leaded to learn diversity through cooperative training. The method is applied in fault diagnosis of power transformer based on Dissolved Gas Analysis. Experiment results show that, the algorithm has higher accuracy than IEC method and BP network. And the performance is more stable than conventional ensemble method, i.e., Bagging and Boosting. )