ECG_Viewer_v1.1.12

How to draw bode plots this is a quite good stuff

说明：  对地震计录进行fk滤波，适用于初学者，可以下载学习(fk filter,FK filtering for seismometer recording is suitable for beginners and can be downloaded for learning)

目标跟踪问题的应用背景是雷达数据处理，即雷达在搜索到目标并记录目标的位置数据, 对测量到的目标位置数据（称为点迹）进行处理，自动形成航迹，并对目标在下一时刻的位置进行预测。 ben文简要讨论了用Kalman滤波方法对单个目标航迹进行预测，并借助于Matlab仿真工具，对实验的效果进行评估。 trajectory.m产生理论的航迹，并绘出 Kalman_filter.m 利用Kalman滤波算法，对目标的航迹进行估计 filter_result.m kalman滤波估计的结果，以及估计误差的均值和标准差曲线(target tracking application context radar data processing, radar in search of the goals and objectives of the record location data, for the measurement of target location data (known as trace points), automatic track, as well as the next target location for the moment forecast. Ben paper with a brief discussion of Kalman filtering method to a single target track forecast and using Matlab tool for experimental evaluation of effectiveness. Trajectory.m have theoretical track, and draws Kalman_filter.m using Kalman filtering algorithm, the target track estimated filter_result.m Kalman filtering results, and the estimated error of the mean and standard deviation of curve)

：在有限区域内分布的稀疏不均的、具有一定分布结构的海量数据集的高效、高精度聚 类问题是一个尚未完全圆满解决的难题。针对Affinity Propagation 聚类算法(AP)的不足之处， 提出了两个改进型的聚类算法(n limited areas of uneven distribution of sparse, has certain distribution structure of the mass datasets of high efficiency, high precision together Problem is a perfectly solved problem has not yet completely. For Affinity Propagation clustering algorithm (AP) of the deficiencies, Put forward the two improved version of the clustering algorithm)

vibration poutre, diagnostic, rdm, bearing, gearbox, induction machine, my target is to make vibratory analysis and to extrac default origine frome current and vibration signal

雷达回波的matlab模拟程序,用来产生雷达回波，非常好用的一个程序(Radar echo of matlab simulation program, which is used to generate radar echo )

灰度 GM（1.1）模型：灰色系统其内部一部分信息已知，另一部分信息未知或不确定。对既含有已知信息又含有不确定信息的系统进行的预测，也就是对在一定范围内变化的、与时间序列有关的灰过程进行 预测。(Gray GM (1.1) model of grey system: the internal part of the information known, the other part of the information is unknown or uncertain. In which there are both known information and uncertain for the prediction information system, is also the process of gray change in a certain range, with the time series related to Prediction.)

Restricted Boltzmann Machine with binary visible and linear/Gaussian hidden units.

This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. (This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. )

雷达仿真的图像处理Matlab程序 雷达仿真的图像处理Matlab程序(Matlab simulation of the imaging radar radar simulation program Matlab image-processing procedures)