simulation5.15

在MATLAB实现的用C实现的KNN算法，输入训练数据文件，和test文件，进行预测分类，并且可以与真实情况比较测试预测正确百分比(Implemented in MATLAB KNN algorithm implemented in C, the input training data file, and test files, to predict the classification and testing with the real situation is more correct percentage of predicted)

倒立摆模型的局部线性化 设倒立摆的摆角范围为[-15,15]度，摆角角速度范围为[-200,200]度/秒，摆角角加速度范围为[-200,200]度/秒2，采用三角形隶属函数对摆角和摆角角速度进行模糊化，摆角初始状态为[0.2,0]。 (Inverted pendulum model of the local linear-based inverted pendulum swinging angle of the range of [-15,15] degrees angle angular range of [-200,200] degrees/sec, angular acceleration angle range of [-200,200] degrees/sec 2, the use of triangular membership function of the pendulum angle and angular velocity fuzzy angles, and angles for the initial state [0.2,0].)

说明：  OFDM_DQPSK matlab source code!(OFDM_DQPSK)

用于partial least square 算法中数据的训练，并得到相应的系数矩阵(Partial least square algorithm for training data and corresponding coefficient matrix)

接触网固有频率计算程序，可根据注释输入接触网参数，利用求结构矩阵特征值的方法求取接触网的固有频率…(Catenary natural frequency calculation program, according to the catenary comment input parameters, the use of structural matrix eigenvalue seeking ways to strike a catenary natural frequency ...)

基于最小错误概率的贝叶斯分类，用于数字识别分类(Based on minimum error probability Bayesian classifier for Digital Identification and Classification)

可以实现条形图和阶梯图形，导入marlab中很容易实现(picture of strip and ladder)

直流信号和阶跃信号的傅里叶分析和频谱图.(Fourier analysis and spectrogram DC signal and step signal.)

用于观察脑电信号全通道数据和多个单通道数据，并且输出相对颅内压参数。函数通过读取nt后缀脑电图文件，并生成GUI界面，可选择全通道观察所选文件脑电信号，也可以选择包含原始EEG脑电信号，40hzEEG，80hz肌电干扰和提取出的40hzEEG信号的多个单通道观察，并且生成颅内压参数。(Used to observe all channels of EEG data and multiple-channel data, and output parameters relative intracranial pressure. 40hzEEG signal function by reading nt suffix EEG file and generates a GUI interface, you can all channels of EEG observe the selected file, or you can choose to include original EEG EEG, 40hzEEG, 80hz EMG interference and extracted a plurality of single-channel observation, and generates an intracranial pressure parameters.)

GMM PARAMETERS AN BE EASILY IMPLIMENTEED USING EM TECHNIQUE.EVENTHOUGH THIS MODEL IS COMPLEX ,E XPECTATION MAXIMAISATION FINDS AN EASYWAY TO CALCULATE THE PARAMETERS IN AN EFFICIENT WAY.