simplex_step

基于Matlab的带通滤波器的设计源码，可以通过此代码，模拟带通滤波器的设计。并附有仿真后的图像。(Matlab-based band-pass filter design source, through this code, analog band-pass filter design. Together with simulation, the image.)

设计的软件锁相环的例子，自己写的，根据原理编的(PLL design example of software that he wrote, according to the principle for the)

naive bayesian classifier in matlab

shabake asabiPerceptron.m

K均值聚类的例子程序，程序中有附带的数据(examples of k-means technology)

多个关于有源电力滤波器的仿真例子，所有例子都有调试完毕，对学习APF有帮助(A plurality of active power filter on simulation example, all the examples have debugging, learning APF helpful)

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.)

L-M神经网络在matlab开发环境中的实现实例，有利于初学者学习与掌握(L-M neural network in the matlab environment development to achieve the example, be helpful for beginners to learn and master)

能量熵的计算，小波包分析提取振动信号中的特征频率，借鉴了主成分分析算法（PCA）。( Energy entropy calculation, Wavelet packet analysis to extract vibration signal characteristic frequency, It draws on principal component analysis algorithm (PCA).)

循环谱密度函数matlab代码，介绍清楚详细，值得借鉴，好东西(Cyclic spectral density function matlab code, clear and detailed description, to be learned, good things)