chongji

《遗传算法理论应用与软件实现》中的初始化MATLAB源代码("genetic algorithm theory and application software," the source code initialization MATLAB)

说明：  利用凯泽窗产生FIR滤波器，可以直接使用，不用其它头文件。(err)

FDTD三维场的叠加画图显示，很不错的程序。希望对大家有用!(Superposition of three-dimensional field FDTD drawing shows a very good program. Hope it useful!)

压缩感知中的贪婪重构算法，包括gomp，CoSamp，Romp，Samp，Sp，Stmp，Swmp(The greedy reconstruction algorithms in compressed sensing include gomp, CoSamp, Romp, Samp, Sp, Stmp, Swmp)

DCDC全桥变换器的闭环simulink模型，电压闭环PI控制(Simulink for full_bridge DCDC.it was controled by PI controler )

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.)

matlab file for image processing

using MonteCarlo Method

matlab simulink model of an uninterrupted power supply

说明：  该设计实现了在发送端使用卷积编码，接收端采用维特比译码(Convolutional coding, Viterbi decoding)