Orthogonal

求解含有有限个变量的函数，维数可以是1000甚至上万维 都可以(Solving function contains a finite number of variables, dimensions can be 1000 on the World Wide Web can be even)

希尔伯特黄变换中英资料，黄先生HHT原文与推荐使用的中文资料，非常有帮助(Hilbert Huang Transform English information Wong HHT original and recommended the use of Chinese data, very helpful)

各种分类算法都包括在里面,有SVAR,ADMM等,在运用时,只需要调用就可以了。(Various classification algorithms are included in the inside, there SVAR, ADMM, etc., in the use, you only need to call on it.)

毫米波雷达算法仿真，程序中对发射信号产生，回波信号的产生，中频信号的产生，滤波进行仿真(Millimeter wave radar, algorithm, simulation)

用有限差分法求解矩形域上的laplace方程的matlab程序(laplace Equation)

基于matlab软件实现等距离离散化为粗糙集约简前准备(matlab Equidistance discretization)

分析橡胶挤出到人工分剪过程中的运动，以适应企业所生产所有规格橡胶为目标；以台达SV2型号PLC为控制核心，采用惠斯通电桥压电式传感器实时采集橡胶重量变化；针对现场加工环境中产生的振动干扰，并分析称重传感器采集到的称重信号；针对分剪系统响应速度与橡胶挤出速度匹配问题导致的不同挤出速度下误差的分布不同，且随着挤出速度的增大导致所分剪橡胶误差也增大的问题，(The movement of rubber extrusion to artificial shearing process is analyzed in order to adapt to the rubber of all specifications produced by the enterprise. Taking the delta SV2 model PLC as the control core and using the Wheatstone bridge piezoelectric sensor to collect the change of rubber weight in real time, the vibration interference produced in the field processing environment and the analysis of the weighing sensor are analyzed. The set of weighing signals; the distribution of errors at different extrusion speeds caused by the matching of the response speed of the shear system to the rubber extrusion speed is different, and the error of the shear rubber increases with the increase of the extrusion speed.)

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.)

Graphic Simulation the movement of swarm of fishes, using gravitational law

matlab基础教程终结 欢迎收藏 遇到问题 可以查询 英文官方教材 和help里面的差不多 是一个总结的pdf文件(matlab Essentials Collection welcome the end of a problem you can check the official teaching English and help inside is almost a summary of the pdf file)