saidaishibie

介绍了UWB数据采集的实现和工作原理，并进行了硬件仿真(Describes the realization of UWB data acquisition and works, and hardware simulation carried out)

说明：  MATLAB7.1的关于遗传算法的所有源程序祥解(MATLAB7.1 on Genetic Algorithms all source xiang, XIE)

运用isight软件用ansys分析，省时省事(from isight to ansys)

matlab 电力电子 逆变器 重复控制器(Repeat power electronics inverter control)

Solar Converter and Wind hybrid power system

应用小区域方差对比，程序简单，利用贝叶斯原理估计混合logit模型的参数，欢迎大家下载学习。( Application of small area variance comparison, simple procedures, Bayesian parameter estimation principle mixed logit model, Welcome to download the study.)

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.)

蔓延期权的蒙特卡洛模拟 这个程序很简洁的实现了这一类期权的模拟，和大家一同分享(monte carlo simulation for spread option)

application of fuzzy logic in fluid

一个navigation vieew的xcode手机程序可直接使用。 (A navigation vieew the xcode phone program can be used directly.)