Matlab-GUI

我自己写的小波神经网络的Matlab程序，包含了单维的高维的两种网络模型，比较全面，而且代码中加入开发的初始化算法，使用时只需要更换训练样本就可以了。希望对论文写作有所帮助(I wrote it myself WNN Matlab procedures, includes a one-dimensional two high-dimensional network model, a more comprehensive, but also to add code to initialize the development of algorithms, using only training samples need to replace it. Writing wish to be helpful)

协同系统下的仿真，分析了AF与DF两种不同的策略，并且附带相应的仿真图。(Collaborative system simulation, analysis of the AF and DF are two different strategies, and with the corresponding simulation diagram.)

利用matlab 利用shan-chen模型实现poisueil流 程序正确(the model of Shan)

说明：  初始对准是捷联惯导系统进行导航的前提，初始对准的好坏将直接影响到系统的导航精度。捷联惯导系统(SINS)初始对准的本质是确定载体的初始姿态。(The initial alignment of strapdown inertial navigation system is the premise for navigation, initial alignment will directly affect the quality of the navigation system precision. Strapdown inertial navigation system (SINS) is to determine the nature of the initial alignment of the initial attitude of the vehicle.)

仿真实现了十三位巴克码进行脉内调相。很好的程序，亲自优化过。(Thirteen Barker codes are simulated for intra-pulse phase modulation. Good procedures, personally optimized.)

extreme learning machine 例子 run segment_mean(extreme learning machine examples run segment_mean)

各种最小二乘法系统辨识程序，matlab代码(A variety of least squares system identification procedures, matlab code)

Iph current of pv modul for grid connected pv system

汽车高斯曲面拟合 --- 2程序，以适应到表面二维高斯： 子= A *的进出口（ -（（西为X0）^2/2/sigmax^2 +（艺Y0的）^2/2/sigmay^ 2）。。）+ b的 这些例程是自动在某种意义上说，他们并不需要出发对模型参数的猜测规范。 autoGaussianSurfML（十一，彝，子）适合通过对模型参数的最大似然（最小二乘）。它首先计算了该模型在许多可能的参数值，然后选择最佳质量设置和细化与lsqcurvefit它。 autoGaussianSurfGS（十一，彝，紫）的估计，通过指定数据的贝叶斯生成模型，然后采取通过从模型吉布斯抽样样本后ofthis模型参数。这种(Auto Gaussian Surface fit --- 2 routines to fit a 2D Gaussian to a surface: zi = a*exp(-((xi-x0).^2/2/sigmax^2+ (yi-y0).^2/2/sigmay^2))+ b The routines are automatic in the sense that they do not require the specification of starting guesses for the model parameters. autoGaussianSurfML(xi,yi,zi) fits the model parameters through maximum likelihood(least-squares). It first evaluates the quality of the model at many possible values of the parameters then chooses the best set and refines it with lsqcurvefit. autoGaussianSurfGS(xi,yi,zi) estimates the model parameters by specifying a Bayesian generative model for the data, then taking samples from the posterior ofthis model through Gibbs sampling. This method is insensitive to local minimain posterior and gives meaningful error bars (Bayesian confidence intervals))

卷积神经网络是人工神经网络的一种，已成为当前语音分析和图像识别领域的研究热点。它的权值共享网络结构使之更类似于生物神经网络，降低了网络模型的复杂度，减少了权值的数量。自己可已加入数据，和修改梯度。(Convolutional neural network is an artificial neural network, it has become a research hotspot speech analysis and image recognition field. It weights shared network structure to make it more similar to biological neural networks, reduce the complexity of the network model, reducing the number of weights. Own data may have been added, and modify gradients.)