Random-Signals-and-Applied-Kalman

实现去趋势波动分析，计算得到波动函数。分段时从两端分别开始避免丢失数据。(Detrended fluctuation analysis of implementation, the calculated wave function. Starting from the ends of the respective segments to avoid loss of data.)

MATLAB的GUI编程图像处理中的基本问题，包括膨胀，腐蚀，线性插值等等一系列的运算。(Image Processing MATLAB-GUI programming in the basic issues, including expansion, corrosion, linear interpolation and so a series of operations.)

matlab灰度二值化经典教程Classic Gray binary matlab tutorial(matlab binary Gray Classic Classic Gray binary matlab tutorial tutorial)

MATLAB信号处理必不可少的滤波器设计全解(MATLAB signal processing filter design essential to the whole solution)

说明：  NN neural network for face recognition supervised feed forward back propagation neural network

本程序可以用来进行非平稳、非线性信号的阶比跟踪分析(Nonstationary and nonlinear properties of order tracking analysis)

多通道欠采样频率测量程序 可以用多通道解模糊测量(duotongdaov.m)

最基本的粒子群算法实现的数据聚类，超简单，觉得对初学者有用(The most basic particle swarm data clustering algorithm, super-simple, feel useful for beginners)

It is the corresponding testing module of the function that is specified in the training phase. test_set is a NxD matrix where N is the number of samples in the test set and D is the dimension of the feature space. true_labels is a Nx1 matrix specifying the class label of each corresponding sample s features (each row) in test_set . adaboost_model is the model that is generated by the function ADABOOST_tr .

BP神经网络容易陷于局部极小值，PSO算法在无约束非线性函数优化方面性能优越，通常可以直接找寻到全局最优解，即使不能搜多到全局最优解，也距离全局最优点不远。当然，基本PSO算法陷入局部极值也是有的。对于这个缺点目前还没有找到比较有效、省市的解决方案。本案例实现利用PSO算法和BP算法共同训练神经网络，先将网络进行PSO算法训练，然后BP算法接着进行小范围精细搜索，PSO算法训练神经网络的本质就是将输出误差函数（即能量函数）看成目标函数，PSO对能量函数进行全局寻找最小值。(BP neural networks are prone to local minimum values. The PS algorithm has superior performance in the optimization of unconstrained nonlinear functions. It can usually find the global optimal solution directly. Even if it can not find more global optimal solutions, it is not far from the global best. Of course, there is also a local extreme of the basic PO algorithm. For this shortcoming, there is no more effective, provincial and municipal solution. This case realizes the use of the SO algorithm and the BP algorithm to train the neural network. First, the network is trained in the SO algorithm, and then the BP algorithm is followed by a small range of fine searches. The essence of the PO algorithm training neural network is to regard the output error function(ie, the energy function) as the objective function, and the PO seeks a global minimum value for the energy function.)