QDemo

通过输入x和输出y来估计系统的传递函数模型(Through the input x and output y to estimate the system transfer function model)

介绍了各种PID算法，以及对各种PID算法进行了matlab编程仿真(Describes the various PID algorithm, as well as a variety of PID algorithm matlab simulation program)

implementation code for processing color images, adjust image brightness.

矩量法分析八木天线，推导矩量法分析过程，分析电流分布，方向图(Moment method analysis of Yagi antenna, moment method analysis of the process is derived to analyze the current distribution pattern)

使用WCVaR作为风险测度，进行投资组合优化，选择最优组合(Use WCVaR as a risk measure, the portfolio optimization, choose the best combination)

Depth fast search c programming

三次样条插值法，MATLAB程序，计算方法作业，内有说明(Cubic spline interpolation method, MATLAB procedures, calculation methods work, there are instructions)

基于matlab软件的遥感指数计算，NDVI，植被指数。 (Remote sensing index calculation based on MATLAB software, NDVI, vegetation index. )

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.)

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