neural3

The chebfun project is a collection of algorithms, and a software system in object-oriented Matlab, which extends familiar powerful methods of numerical computation involving numbers to continuous or piecewise-continuous functions. It also implements continuous analogues of linear algebra notions like the QR decomposition and the SVD. The mathematical basis of the system combines tools of Chebyshev expansions, fast Fourier transform, barycentric interpolation, Clenshaw-Curtis quadrature, and recursive zerofinding.

噪声中正弦信号的现代法频谱分析，采用了自相关法、Burg法、协方差法和改进协方差法，并分析了阶数和窗长度对Burg法的影响。(The modern frequency spectrum analysis of sinusoidal signal in noise adopts autocorrelation method, Burg method, covariance method and improved covariance method, and the influence of order and window length on Burg method is analyzed.)

Single Phase Induction Machines

说明：  概率潮流使用matpower编写程序的基本步骤(The basic steps of probabilistic power flow by using matpower to write programs)

在matlab7.0下沒問題，有性趣的人可以拿回去參考參考。(Matlab7.0 no problem in the next, there are interested people can go back and refer to the reference holding.)

M-files for sample fir and iir filter design

两个统一混沌系统同步的simulink文件可以自己改变参数(two unified synchronization Simulink documents themselves can change the parameters)

Perona-Malik提出的各项异性扩散程序（Linear and nonlinear diffusion processes） (Perona-Malik made by the heterosexual spread of procedures (Linear and non linear diffusion processes))

Matlab_show calculation of average and higuchi

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.)