slm

说明：  非常形象的QPSK simulink 例子。(Very image of QPSK simulink examples)

A method for classification of image using svm kernel

检测脸部的有无(Detect whether the face)

本程序主要针对LMS自适应算法进行了学习，包括收敛曲线，学习曲线，平均收敛轨迹。(This program mainly studies LMS adaptive algorithm, including convergence curve, learning curve, average convergence trajectory.)

说明：  深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)

Forecast result example

自己写的信噪比计算工具snr2.m snr2(s2ana,fs,f0,fl,fh,bl,bh) s2ana: 待分析信号 fs:采样频率 f0:信号频率 [fl,fh]:信号能量分散带(Their signal to noise ratio calculation tool written in snr2.m snr2 (s2ana, fs, f0, fl, fh, bl, bh) s2ana: to be analyzed signal fs: sampling frequency f0: signal frequency [fl, fh]: the signal with energy dispersive)

一个很好的滤波器程序，是用MATLAB编写的M文件，虽然简单，但很有借鉴意义。(a good filter procedure is the use of MATLAB M documents, simple, But a useful one.)

此程序为用matlab编写的基于最小二乘法通过求解广义矩阵特征值及特征向量进行椭圆曲线拟合程序。 程序中x,y分别为采样点的横纵坐标。生成的结果矩阵为椭圆曲线aX^2+bXY+cY^2+dX+eY+f=0中的各项系数。(This program was written with matlab by solving the generalized least square method based on matrix values and eigen vectors of the elliptic curve fitting procedure. Process x, y, respectively transverse and longitudinal coordinates of sampling points. The results generated matrix of elliptic curve aX ^ 2+ bXY+ cY ^ 2+ dX+ eY+ f = 0 in the coefficients.)

Digital version of Fractional-Order PID Controller (DFOC) of the form: C(s) = K + Ti/s^m + Td*s^d for given sampling period Ts [sec]. For more details and help write: >> help DFOC