truss-structure

说明：  信号处理,是一本国处优秀教材的附带程序源码,相信对大家有用!(Signal processing, is a national Department of incidental teaching procedures excellent source, I believe it useful to everyone!)

说明：  电动汽车控制策略研究 天津大学电气与自动化工程学院 通过MATLAB/SIMULINK仿真软件对各种控制方案和再生制动策略 分别进行了仿真研究和对比，得出各种控制策略能够提高电动汽车性能的结论。 同时将仿真结果与实验室和台架实验系统的实验结果做了相应的研究与比较，进 一步验证了上述控制策略的可行性。 (Study on Control Strategy for Electric Vehicle )

My code for connecting ORACLE and MATLAB

ｌｄｐｃ的论文，希望对初学者有帮助，物品也是刚开始的。(LDPC papers, and they hope to be helpful for beginners, is also the beginning of articles.)

matlab小程序50例，简单但是涉及的知识广泛，适合初学者学习借鉴，好东西啊(matlab small program of 50 cases, a simple but broad knowledge involved, for beginners to learn from, a good thing ah)

passion方程的第一类边界条件的数值解，边界条件过于复杂而无法求得解析解。这就需要借助于数值法来求电磁场的数值解。有限差分法便是一种比较容易的数值解法。(Numerical Solution of the first boundary condition passion equations and boundary conditions are too complex to be obtained by analytical solution. This requires the help of numerical methods to find numerical solution of electromagnetic fields. Finite difference method is a relatively easy numerical method.)

两个OFDM的仿真程序，大家可以对比看一下。(Two OFDM simulation procedures, we can look at the contrast.)

这是龚纯《精通MATLAB最优化计算》随书源码(M文件)。基于MATLAB优化工具箱，代码包含的内容有：牛顿法等无约束一维极值问题、单纯形搜索法等无约束多维极值问题、Rosen梯度投影法等约束优化问题、L-M法等非线性最小二乘优化问题、线性规划、整数规划、二次规划、粒子群优化、遗传算法。(This is pure Gong " Mastering MATLAB optimization calculations," with the book source (M file). Based on MATLAB Optimization Toolbox, the content code contains: Newton and other non-binding one-dimensional extremum problems, such as simplex search method for unconstrained multi-dimensional extremum problems, Rosen gradient projection method and other constrained optimization problems, LM non-linear law least squares optimization problem, linear programming, integer programming, quadratic programming, particle swarm optimization, genetic algorithm.)

数字信号处理第三版对应实验的所有matlab程序(Digital signal processing matlab third edition of the corresponding test procedures for all)

此为一篇介绍mcmc方法的外文文献 非常适合刚刚接触mcmc方法的初学者(This paper introduces the readers of the Proceedings to an important class of computer based simulation techniques known as Markov chain Monte Carlo (MCMC) methods. General properties characterizing these methods will be discussed, but the main emphasis will be placed on one MCMC method known as the Gibbs sampler. The Gibbs sampler permits one to simulate realizations from complicated stochastic models in high dimensions by making use of the model’s associated full conditional distributions, which will generally have a much simpler and more manageable form. In its most extreme version, the Gibbs sampler reduces the analysis of a complicated multivariate stochastic model to the consideration of that model’s associated univariate full conditional distributions.)