Tool-box-for-fuzzy

使用matlab产生混沌时间序列的源代码(use Matlab chaotic time series generated source code)

dc motor of simulink and pid controller

基于贝叶斯的抠像算法，通过一个最大似然的标准去估计透明度(A Bayesian Approach to Digital Matting，uses a maximum-likelihood criterion to estimate the optimal opacity)

说明：  LVQ神经网络的预测，可以用来进行人脸识别(lvqnetwork，a important kind of network)

利用matlab求解超定方法，使用最小二乘法求解平面程序(Using the least squares method for solving overdetermined equations, find plane program)

跳跃扩散模型结合条件异方差自回归模型的参数估计(Jump diffusion model combined with conditional heteroskedasticity self-parametric regression models estimate)

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.)

bch编译码仿真程序。信息符号15bits，码字符号11bits。(bch decoding simulation program. Information symbols 15bits, code word symbols 11bits.)

方程求根的源代码，matlab编写的，值得收藏，欢迎大家踊跃下载，为中国的科研事业做贡献(Equation Root of the source code, matlab prepared and worthy of collection, enthusiastically welcomed the U.S. download, in order to make China' s contribution to the cause of scientific research)

由于小波函数没有显示表达，此程序通过数值方法将其表示出来(wavelet function)