rsadec

A skyhook surface sliding mode control method was proposed and applied to the control on the semi-active vehicle suspension system for its ride comfort enhancement. A two degree of freedom dynamic model of a vehicle semi-active suspension system was given, which focused on the passenger’s ride comfort perform-ance. A simulation with the given initial conditions has been devised in MATLAB/SIMULINK. The simula-tion results were showing that there was an enhanced level of ride comfort for the vehicle semi-active sus-pension system with the skyhook surface sliding mode controller.

说明：  一个matlab实例程序，可以用来检验和注册成会员(Matlab example of a procedure can be used to test and registered members)

利用matlab自动绘制某气象站风玫瑰图，并将静风频率和16方位风向频率标在图上(use matlab to plot wind rose)

关于高阶循环累积量的的程序，用于信号调制识别(high order cyclic cumum)

说明：  二维MATLAB有限元程序，编程文件，内容很详细。(Two dimensional matlab finite element program)

通信系统MATLAB经典设计源码程序Classic design of communication systems MATLAB source program(Communication system design source program MATLAB Classic Classic design of communication systems MATLAB source program)

About sift algorithm

很好的一本matlab参考书，上传个大家，供大家参考，下载使用(matlab book)

it a matlab scource code to simulate sfdma

分析了支持向量回归机在能源需求预测中的优势,确定了输入向量集合和输出向量集合,建立了基于Matlab技术的SVR能源需求预测模型.对我国1985-2008年能源需求相关数据进行模拟与仿真,并对中国2010年和2020年能源需求量进行预测.研究结果表明:一是中国未来对能源的需求量逐渐增加,从2010年的330400万吨标准煤上升到2020年418320万吨标准煤,年均增长率为2.39％；二是在解决我国能源系统小样本.非线性及高维模式识别问题中SVR比BP神经网络等方法有更高的预测精度.(Support vector regression analyzes advantage in energy demand forecast to determine the set of input vectors and output vector set, established SVR energy demand forecasting model based on Matlab technology. Energy demand for our 1985-2008 related data modeling and simulation , and China in 2010 and 2020 energy demand forecast results show that: First, China s increasing demand for energy in the future, from 3.304 billion tons of standard coal in 2010 rose to 4.1832 billion tons of standard coal in 2020, average annual growth rate of 2.39 second is to solve our energy system small sample nonlinear and high dimensional pattern recognition problem SVR higher prediction accuracy than the BP neural network method.)