Introduction-to-using-Simulink-in-MatLab

倒立摆 Simulink模型 机理模型 (Inverted pendulum Simulink model mechanism model)

BPSK调制及其解调过程实现的matlab仿真.(BPSK modulation and demodulation process to achieve the matlab simulation.)

Sliding Sum Averaging Filter Smoothes data by sliding it past itself, one sample at a time, and summing. The length of the output is the same as the length of the input. The output will be normalized. Equivalent to a moving average (lowpass) filter, but more efficient.

Finding a parameter "kappa" in the distribution of von Mises-Fisher

关于机器人舒适度区域的研究文章，外国人写的。(Comfort zone on the robot research articles written by foreigners.)

this is a code to calculate the moment invariant of images. the values which come out are capable of removing rotational, scaling, transnational variation in image given as input.classification can be done with the help of it.

matlab图像特征提取程序。。。图像特征统计及计算(matlab image feature extraction procedures. . . Statistics and computing image features)

说明：  牛顿公式、修正牛顿公式和斜激波关系式三种计算压力系数方法比较程序MATLAB源码(Newton' s formula, modified Newton formula and oblique shock relation three calculation pressure coefficient comparison procedures MATLAB source)

sparsify is a set of Matlab m-files implementing a range of different algorithms to calculate sparse signal approximations. Currently sparsify contains two main sets of algorithms, greedy methods (collected under the name of GreedLab) and hard thresholding algorithms (collected in HardLab). See ALGORITHMS below for a list of available algorithms.(sparsify is a set of Matlab m-files implementing a range of different algorithms to calculate sparse signal approximations. Currently sparsify contains two main sets of algorithms, greedy methods (collected under the name of GreedLab) and hard thresholding algorithms (collected in HardLab). See ALGORITHMS below for a list of available algorithms.)

说明：  基于长记性特征的时间序列预测模型，很好用，准确度优于普通神经网络，我自己一直在用(Characteristics based on long memory time series forecasting model, the good, the accuracy is better than an ordinary neural network, I have been using)