matlab-chapter5

Malab实现最佳线性优化，(Malab realize the best linear optimization,)

说明：  本文涵盖了matlab的全部内容，简单易学，是快速入门的最直接办法。(This article covers the entire contents of matlab, easy to learn, is the most direct way to quick start.)

heat equation mixte parameters matlab file

本程序用matlab仿真卫星及轨道，希望对大家的学习有所帮助(This procedure simulation using matlab and satellite orbit, and they hope to be helpful to study the U.S.)

一个学习matlab的课件，对于新入门的学习者比较有帮助(Matlab a learning courseware, the new entry for more helpful to learners)

robotics plot for obstacle avoidance

一个MATLAB/Simulink模型，关于电动汽车动力电池健康状态（State of Health； 建模基于SOC估计； 模型比较简单，原理清楚，适合对电动汽车动力电池SOH的估计有兴趣的初学者参考学习，学习前请阅读2到3篇动力电池SOH估计方面论文，这样对理解模型更有帮助。(A MATLAB/Simulink model, on the electric vehicle power battery health status (State of Health Modeling based on SOC estimation Model is relatively simple, the principle is clear, suitable for beginners interested in electric vehicle power battery SOH estimation, it will be better to read 2 to 3 SOC estimation papers about battery SOH estimates.)

Three-phase thyristor converter 变频器，来自mathworks 程序。(Three-phase thyristor converter inverter, from MathWorks procedures.)

this is about DTFT.DTFT s matlab code and it is for Discrete time fourier transform.

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.)