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钻井船悬臂梁loadchart计算 ,计算悬臂梁升出船体所能承受的井心与立根盒的最大载荷。(Calculate the cantilever rigs loadchart)

matlab要求编写函数，并画出图的matlab习题，供大家参考(prepared Matlab functions, and draws a plan of Matlab Exercise, for your reference! !)

提供了几种最小二乘法程序包括：批处理最小二乘参数估计，递推最小二乘参数估计，遗忘因子递推最小二乘参数估计，递推增广最小二乘参数估计。(Provides several least squares procedures include: Batch least squares parameter estimation, recursive least squares parameter estimation, forgetting factor recursive least squares parameter estimation, Recursive Extended Least Squares Parameter Estimation.)

说明：  很简单的对图像进行滤波而使用的维纳滤波器使用代码(Very simple filter on the image using the Wiener filter used by the code)

完整的matlab教程，简明易学，适合初学者入门。(Complete matlab tutorial, easy to learn, suitable for beginners.)

joint adaptive modulation and user scheduling scheme

说明：  计算棱镜SPR传感器透射率，包括角度扫描和波长扫描两种模式。(Calculation of transmittance of prism SPR sensor)

说明：  自己总结搭建的模糊PID控制程序，根据具体的对象可以自己设置相应的参数(Sum up their fuzzy PID control procedures set up, according to the specific object can own set of parameters corresponding)

基于最简单的直接转矩控制的matlab程序框图，经试验，完全可以正常运转(Based on the most simple and direct torque control matlab block diagram, the test, can operate normally)

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. )