KNN1

这是小波的命令行方式，汇集了大量的小波工具中函数(This is a wavelet from the command line, bringing together a large number of wavelet tools in the Functions)

本程序可以使8盏灯循环点亮，并调节点亮速度。方便简单。(This program allows eight lamp light cycle, and adjust the light speed. Convenient and simple.)

SPAMS稀疏优化工具箱,采用VC++编写，面向MATLAB版本的。(This is the source code of the software SPAMS, which needs to be compiled before begin used.)

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.)

本书详细讲述了如何用MATLAB进行程序设计，如何编写清楚、高效的程序。书中强调了自上而下的程序设计方法、函数的使用、MATLAB内部工具的使用和数据结构，并指出了一些使用技巧和编程者常犯的错误。(This book describes in detail how to use MATLAB for programming, how to write clear and efficient procedures. The book emphasizes top-down program design, function, use, MATLAB uses internal tools and data structures, and pointed out some tips and common mistakes programmers.)

说明：  一种从采样数据构造概率密度函数的方法 sample to pdf (sample to pdf)

牛顿拉夫迭代算法，有兴趣的可以看看，相信会有用吧(Love Newton iterative algorithm, interested can see, I believe it will be used)

we address the design of economical recursive generalized comb filters (GCFs) by proposing an ef- ficient technique to quantize the multipliers in the z-transfer function employing power-of-2 (PO2) terms. GCFs are efficient anti-aliasing decimation filters with improved selectivity and quantization noise rejection performance around the so-called folding bands with respect to classical comb filters. The proposed quantization technique guarantees perfect pole–zero cancelation in the rational z-transfer function of the GCFs, thus totally avoiding instability problems. Moreover, we propose the use of a simple droop compensator for the sake of recovering the passband droop distorting the useful digital signal in the baseband. A design example is proposed with the aim of showing the application of the proposed technique, and a practical architecture of a sample third-order GCF is discussed.

可以解出一个线性矩阵不等式中的未知矩阵。(Can solve a linear matrix inequality in the unknown matrix.)

主要的粒子群优化方法的matlab实现,粒子群优化算法的主要理论的实现方法(the methods of the PSO)