vector_control

matlab中常用电法各种装置的电磁正演计算(matlab often electricity law of electromagnetic devices forward calculation)

求解对问题的matlab程序直接运行就可的到结果(Matlab solving the problem can be directly run to the results)

用于计算输电网的潮流，充分考虑了线路中的变压器，并且对变压器模型设定了不同的输入方式，较为灵活的输入。(For the calculation of the trend on the transmission grid, give full consideration to the line transformer and the transformer model set a different input methods, more flexible input.)

matlab代码，用来实现adaboost算法的代码，具体的该算法的实现还需要进行一些细节的修改，确保程序的正确运行。(matlab achieve adaboost funtion)

Transmission line control example created in MATLAB Simulink enviroment , This model illustrate the exact modelling of transmission lines in real scenarios.

A custom view for an item in the contact list.

用matlab画矩形排样图。【WL数组中第一列至第四列分别为：矩形长、矩形宽、矩形左下角横坐标、矩形左下角纵坐标】(Matlab to draw rectangles layout diagram. 【 probably in the first column to the fourth column in the array are: rectangular rectangular long, wide, abscissa rectangular corner, rectangular corner ordinate 】)

说明：  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.)

使用matlab对ldpc码进行仿真，得到在高斯白噪声和bpsk调制下的误码率曲线 (matlab bp awgn)

example 2 of nonlinear control