CABLE tv

说明：  2006标准PSO程序，可以用于系统的优化，请大家式一式。有什么好的意见提出来，(2006 standard procedure can be used for system optimization, please-a type. What good ideas put forward, thank you)

离散Hopfield神经网络的分类-高校科研能力的评价，有Hopfield神经网络的详细的M文件程序，适合初学者(Discrete Hopfield neural network classification- the evaluation of university research capacity, the Hopfield neural network of the M-file program, suitable for beginners)

说明：  图像复原工具包，希望能为研究图像复原的同学提供帮助(Image Restoration Kit, which seeks to recover the image study to help students)

This programs detect fire in image

A New Image Filter for Impulsive Noise Based on Rough Sets

matlab codes for statistics analysis

说明：  Feedback quantization in LTE:

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.)

说明：  MATLAB时频分析工具箱源代码，包括使用说明各种函数库等等(Toolbox MATLAB source code for time-frequency analysis)

DFIG PID CONTROL ,MODELING DFIG IN STATE SPACE MODELAND CONTROL WITH PID CONTROL