四面体单元

這個程式可以顯示 3~100 的 prime number，很適合新手練習的範例程式，附上源碼可以修改內部細節，多做驗證。()

自己写的稀疏矩阵类，包括存储、访问以及基本的数学运算，希望大家批评指正！(Sparse matrix classes, including storage, access, and basic mathematical operations, we hope to criticism!)

说明：  lingo软件分享,lingo是专门用于解决规划问题的数值计算工具，操作简便(Lingo Software Sharing, Lingo is a numerical calculation tool specially used to solve planning problems. It is easy to operate.)

用于计算太阳辐射的模型，该模型计算精度好，方法简单(Used to calculate the solar radiation model, the model accuracy, the method is simple)

Hansen最新（2008）修订后的关于反演正则化方法的电子书与相应Matlab程序(Hansen the latest (2008) as amended on the inversion method is the e-book with the corresponding Matlab program)

gmres 方法。视图求解线性方程组A*x=b的解x。nXn的稀疏矩阵A必须是方程且应是大型稀疏矩阵。列向量b的长度必须为n。(gmres method. View solving linear equations A* x = b the solution x. nXn sparse matrix A must be equation and should be large sparse matrix. The length of the column vector b must be to n.)

对于应用数学专业学习非线性混沌控制的学生非常有用，可以下载并尝试自己动手改变后半部分，就能应用于新系统中(For students applied mathematics learning nonlinear chaotic control is very useful, you can download and try yourself to change the second half, will be able to be applied to the new system)

说明：  matlab的adi方法求解偏微分方程，精确度较高(ADI method of MATLAB for solving partial differential equations)

为了提高三级倒立摆系统控制的响应速度和稳定性,在设计Mamdani 型模糊推理规则控制器控制倒立摆系统稳定的基础上, 设计了一种更有效率的基于Sugeno 型模糊推理规则的模糊神经网络控制器。该控制器使用BP 神经网络和最小二乘法的混 合算法进行参数训练,能够准确归纳输入输出量的模糊隶属度函数和模糊逻辑规则。通过与Mamdani 型控制器的仿真对比, 表明该Sugeno 型模糊神经网络控制器对三级倒立摆系统的控制具有良好的稳定性和快速性,以及较高的控制精度。(In order to improve the three-level control of inverted pendulum system response speed and stability, in the design of Mamdani-type fuzzy inference rules of the system controller to control the stability of inverted pendulum on the basis of a more efficient design based on Sugeno-type fuzzy inference rules of fuzzy neural network controller. The controller is the use of BP neural network and hybrid least squares training algorithm parameters can be accurately summed up the amount of input and output fuzzy membership function and fuzzy logic rules. Mamdani-type controller with a simulation comparison shows that the Sugeno-type fuzzy neural network controller for the three-tier control of inverted pendulum system with good stability and fast, as well as a higher control precision.)

使用Matlab编程，对教材74页例3.3.1进行编程仿真。其中，正电压A的值、噪声方差值、每个码元周期内的采样点数N自行设定（可设置为可调的变量）。噪声值可使用Matlab中产生高斯随机数的函数来进行仿真。贝叶斯检测判决式中，先验概率P(H1)=P(H0)，判断错误的代价因子设为1，判断正确的代价因子设为0. 按照上述参数的设定进行仿真，实现对仿真数据的贝叶斯检测判决；循环生成仿真数据，并进行判决结果的统计，即记录判决正确的次数，估计正确判决的概率。(The use of Matlab programming, teaching materials 74 pages of 3.3.1 programming simulation. Among them, the value of positive voltage A, the variance of noise, the number of sampling points in each symbol cycle (N) can be set by itself (adjustable variable). Noise values can be simulated using the function of generating Gauss random numbers in Matlab. In the Bias detection decision, the prior probability P (H1) =P (H0), the factor of error judgment is set to 1, and the correct cost factor is set to 0. According to the simulation parameters and realize the judgment of Bayesian detection simulation data; generating cycle simulation data, statistics and decision, which records the correct number of judgment, the estimated probability of correct decision.)