lm

分别采用Roe格式和AUSM格式求解一维黎曼问题，并将计算结果与解析解进行比较，发现这两种计算格式都能获得高的计算精度。(Roe scheme were used in this procedure and AUSM format to solve a 1-D RIEMANN problem, and numerical results were compared with the analytical solution, the results can be seen from these two formats have a solution with high accuracy.)

计算动力学研究，应力波引起的裂纹扩展问题，供大家学习(Kinetics calculations, crack propagation problems caused by stress wave for everyone to learn)

关于常用的FORTRAN算法，该书有关于线性方程组的求解，矩阵特征值的计算，常微分方程组的求解等(FORTRAN on the commonly used algorithms, the book on the linear equations, eigenvalue calculation, solving ordinary differential equations, etc.)

自己编的5种调制信号，通过反复训练模板能有较高的识别率，DC-DC部分采用定功率单环控制。( Own five modulation signal, Through repeated training VxODOGFlate have higher recognition rate, DC-DC power single-part set-loop control.)

用C语言编写的1D-FDTD程序，含一阶mur边界，附结果matlab画图程序(Using C language 1D-FDTD procedures, including an order Mur boundary, with results of MATLAB drawing program)

说明：  利用GA遗传算法解决欺骗函数最优问题，具体问题描述如下，如有问题请与我联系(The deceptive functions are a family of functions in which there exists low-order building blocks that do not combine to form the higher-order building blocks. Here, a deceptive problem that consists of 25 copies of the order-4 fully deceptive function DF2 is constructed for this paper. DF2 can be described as follows: f(0000)=28 f(0001)=26 f(0010)=24 f(0011)=18 f(0100)=22 f(0101)=6 f(0110)=14 f(0111)=0 f(1000)=20 f(1001)=12 f(1010)=10 f(1011)=2 f(1100)=8 f(1101)=4 f(1110)=6 f(1111)=30 This problem has a maximal function value of 750.)

最小二乘法和支持向量回归机相结合无线传感网络节点定位的源码.(The source of The least squares and support vector regression combining wireless sensor network node positioning . )

说明：  相关性矩阵源代码，用于计算已知数据的相关性系数(Relevance matrix source code, used to calculate the correlation coefficient of known data)

ADAM （Adaptive Moment Estimation）是另外一种自适应学习率算法，它结合动量梯度 下降法，在不同参数方向上采用不同学习率，保留前几次迭代的梯度，能够很好 的适应于稀疏数据。(ADAM (Adaptive Moment Estimation) is another adaptive learning rate algorithm, which combines momentum gradient. The descent method, which uses different learning rates in different parameter directions and retains the gradients of previous iterations, is very good. It is suitable for sparse data.)

最小二乘法回报潮汐水位，根据dooson数回报潮汐，并计算调和常数，余水位，高潮和低潮的数值。(Least squares method returns tide level, tidal according dooson return number, and calculate the harmonic constants, values ​ ​ more than the water level, high tide and low tide.)