zizuokalman

说明：  异步电机的SVPWM闭环的DTCsimulink文件，可以闭环正常运行，波形还行(Asynchronous motor SVPWM closed-loop dtcsimulink file, can be closed-loop normal operation, waveform is OK)

Duda《模式分类》第二版第1、3、5章部分课后习题和上机题的解答和程序代码(Duda pattern classification, the second edition of the first chapter 1,3,5 some after-school exercises and answers questions on the machine and program code)

参数为：载波频率f0为20MHz，采样频率为4倍f0，脉宽为1us ，脉冲周期为20us 。其中：(Parameters: carrier frequency f0 is 20MHz, the sampling frequency is 4 times f0, a pulse width of 1us, the pulse period is 20us. Of which:)

matlab 画viviani曲线+绘制分子结构+最小二乘拟合+求信号包络线+滤波器设计（一次为大家提供5个matlab完整程序，能调试准确的结果仿真图）(matlab draw viviani curve++ least-squares fit to draw the molecular structure of++ request signal envelope filter design)

在AWGN信道下的 BPSK 和OOK的仿真和误码率 程序在word文件最后(BER Performance of BPSK in AWGN Channel)

一个很好的扩频产生程序，可以帮你完成扩频通讯。(Spread spectrum have a very good program, can help you achieve spread-spectrum communications.)

本程序详细地描述了TD-SCDMA的Turbo编码器和Turbo译码器，对大家的学习很有帮助(This procedure is described in detail TD-SCDMA' s Turbo encoder and Turbo decoder, to all of us to learn very helpful)

运用自相关法，周期图法和burg法分析加噪信号功率谱并比较(The use of auto-correlation method, periodogram analysis burg and noise signal power spectrum and compare)

program for power system

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.)