buildgui

practice of statistics with the help of matlab using ANOVA

Phase unwrapping methods

光伏mppt 可生成pv曲线 m函数(The photovoltaic mppt pv curves m function can be generated)

在MATLAB环境下滤除噪声，并对声音进行分析和处理，(Filtering noise in the MATLAB environment, and sound analysis and processing,)

NVH用7自由度模型，simulink，可重新设定参数(NVH model with seven degrees of freedom, simulink, can be re-set parameters)

说明：  应用matlab gui开发的信号分析小程序，便于大家学习gui(Matlab gui applications developed by small signal analysis procedures, to facilitate U.S. study gui)

这是可以解决tsp问题的遗传算法的matlab，产生初始群体是没采用改良圈法代码，有测试数据，可供参考学习(this is a generic algorithm for tsp)

圆形垂直载荷作用下的弹性半空间的matlab有限元计算源程序，配套书籍是《ansys分析与matlab编程：徐荣桥》(Matlab finite element calculation of the elastic half-space under the circular vertical loads source, supporting books ansys analysis and the matlab programming: Xu Rong Bridge " )

微网联网模式下pq控制仿真，供大家一起参考学习。(Pq control simulation the micro mesh networking mode, the reference for everyone to learn.)

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.)