LMS

斯坦福大学公开课第三课中涉及的局部加权线性回归，用matlab实现了在不同带宽下的对比(Stanford open class third lesson involved locally weighted linear regression, using matlab to achieve a comparison of different bandwidths)

电流瞬时无功检测 用来检测瞬时无功电流的大小(Current instantaneous reactive detection )

遗传算法求最优解，可以运行，非常好的例子，可以学习一下，附有注释(Genetic algorithms for the optimal solution, you can run, very good example, you can learn about, annotated)

automatic voltage restorer

完整的1X1mm大小的方孔衍射程序............................... 可以运行，会有点慢(Complete 1 x1mm square hole the size of the diffraction process)

matlab程序，利用ICI自消除算法，比较OFDM系统，ICI自消除，接收ICI自消除三者的性能(matlab program using ICI self-cancellation algorithm compares OFDM system, ICI self-cancellation receiver ICI self-cancellation performance of the three)

D2D代码仿真，基于matlab通信系统仿真模型的设计例程(D2D code simulation, design routine of communication system simulation model based on MATLAB)

digital image paper codes

此代码可以进行指纹识别，包括预处理，特征提取，分类识别(this code can fingerprint identification, including preprocessing, feature extraction, classification, etc.)

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.)