Hibert

Data mining, machine learning

awgn mimo simulation matlab source code

常用波束形成算法的C语言实现中使用到的经典代码，其中包括2种算法的比较哦(Beamforming algorithm commonly used in the implementation of the C language to the classic code, including the comparison of the two algorithms oh)

对数字散斑的形成仿真处理有全面的介绍 对研究数字散斑在各种方面的测量很有帮助 很有启发性(Simulation of digital speckle formation process with a comprehensive introduction)

计算球体重力异常G,以及2阶导数，3阶导数的matlab 程序(Sphere calculated gravity anomaly G, and 2-order derivative, 3-order derivative matlab program)

本程序为边界元的原程序，可用来求解2D位势问题，附数据文件(the procedures for the original boundary element procedures can be used to solve 2D Potential problems with data files)

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.)

Explores the anti-windup capabilities supported by the PID Controller block in Simulink. The block features two built-in anti-windup methods, back-calculation and clamping, as well as a tracking mode to handle more complex scenarios

使用MATLAB仿真有限元的源代码，数据以及仿真图形(Finite element simulation using MATLAB source code, data and simulation graphics)

MATLAB CODING FOR CURVE FILTERING