Two-dimensional-random-number

矩形域上的二阶椭圆差分格式，适合偏微分方程的学习，是有限差分格式(this is the equetion about the partial differetial equotion)

说明：  L型，超声传感，局放阵列，L型增广矩阵束算法二维(L-type, ultrasonic sensor, PD array, L-type augmented matrix beam algorithm, 2D)

ESG算法和CARMA-GI算法对CARMA模型未知参数的辨识(CARMA_GI.m for the ESG and CARMA-GI algorithms for the CARMA models)

MATLAB 牛顿下山法的一个小程序，原理就不详细说了。因牛顿迭代法受初值选取的限制，为防止迭代发散，对迭代过程再附加一项要求:|f(x(k+1))|<|f(x(k))|,将牛顿法迭代的结果:x(k+1) =x(k)-f(x(k))/f (x(k))和前一近似值x(k)适当加权平均做为新的改进值:x(k+1)=u*x(k+1) +(1-u)*x(k),其中u(0<> 迭代时u取1开始，逐次减半计算，直至附加要求符合为止。实例计算中x(k)=x(0)不变，只更新u和x(k+1)，直至:|f(x(k+1))|<|f(x(k))|（即|f(x(1))|<|f(x(0))|)，然后更新下山因子为u=1,继续以牛顿法迭代。(MATLAB Newton-down a small program, the principle is not explained in detail. Newton iteration by the initial value of the selected limit, in order to prevent additional iterative divergence, the iterative process a request: | f (x (k+1)) | < | f (x (k)) | Newton iteration Results: x (k+1) ' = X (k)-f (x (k))/f' (x (k)) and a previous approximation x (k), appropriately weighted average as a new improved value: x (k+1 ) = u* x (k+1) ' + (1-u)* x (k), where u (0 < u> iteration take successive calculated at 50 until the additional requirements found so far. examples of calculation of X ( k) = x (0) unchanged profile u and x (k+1, hungry) until: | f (x (k+1)) | < | f (x (k)) | (i.e. | f (x (1)) | < | f (x (0)) |), then profile downhill factor for u = 1, to continue to Newton iteration.)

基于matlab编程，仿真。matlab编程百例有助于熟悉matlab基本编程命令，最好成为编程高手。小数据量法求lyapunov指数源程序，可以减轻做混沌控制与同步研究的编程苦恼。(Matlab-based programming, simulation. 100 cases of familiar programming matlab help matlab command of basic programming, preferably as a programming expert. Method of small amount of data source lyapunov index can be done to alleviate study of chaos control and synchronization of programming distress.)

Neural control (reinforcement learning) for tanker heading

通过一点来选取图像区域，手动选取图像中的一点，可以讲这个点周围的一定区域提取出来(Through a point to the image area，manual one point in a image，then we can get an area around this point)

it s sort and get an array then it deliver sorted array to user

说明：  SGA（Simple Genetic Algorithm）是一种强大的智能多变量优化算法，它模仿种群繁殖规律来进行优化。本SGA可以优化变量，求最小值,最大值（当把函数倒数也就求最小值） 并且支持浮点编码，grey编码，二进制编码；轮赌法选择，锦标赛选择；单点交叉，均布交叉，浮点交叉；单点变异，浮点变异；调用时Genetic（目标函数名） 使用SGA时，首先需要一个目标函数（像AimFunc.m），该函数返回适应度输入变量为待优化变量x输出为一个适应度。然后修改Genetic.m中可以修改的地方(SGA (Simple Genetic Algorithm) is a powerful smart multi-variable optimization algorithm, which mimics the reproduction of law to be optimized. The SGA can be optimized variables, minimum, maximum, (when the function will last for the minimum) and supports the floating-point encoding, grey code, binary code round of betting method selection, tournament selection single-point crossover, uniform crossover , floating-point crossover single point mutation, floating-point mutation called Genetic (objective function name) the use of SGA, first of all need an objective function (like AimFunc.m), the fitness function returns the input variables to be optimized for the output variable x a fitness. Can be modified and then modify the place Genetic.m)

小波与希尔伯特-黄变换结合分析脑电信号，先进行频域分解，再进行hht分析(wavelet and hht transform, analyze eeg)