Data-Generation-Sample-1-a-2

聚类分析技术已经广泛应用于基于内容的图象信息挖掘领域，该技术提高了图象检索的速度和质量。K-均值算法和自适应算法是两个典型的聚类分析算法()

非常好的关于粒子滤波的matlab源程序，值得拥有(Very good source on the particle filter, is worth having)

application receiving bets on the numbers game and rewards Correct diagnoses.

广义预测控制算法非常详细仿真matlab(GPC matlab codes )

关于混合储能的充放电过程的simulink仿真，能实现稳定输出电压的方法(About charging and discharging process of mixing the energy storage simulink simulation, to achieve a stable output voltage method)

基于MATLAB_一种IIR数字带通滤波器的设计与仿真(Figures based on MATLAB_ a band-pass IIR filter design and simulation)

Model of OFDM transmiter based on System Generator in Matlab

介绍了小波变换的基本应用，采用小波滤波的方法实现对一组数据的处理，处理结果表明，选取合适的小波基函数以及分解层数才能够达到理想的效果(The basic application of wavelet transform, wavelet filtering method to achieve a set of data processing, processing results show that selecting the appropriate wavelet basis function and decomposition level to be able to achieve the desired results)

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.)

Kohonen MAP Self Organized