fano

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))

数值计算常用程序 牛顿法了 阿达姆斯了 雅克比 等十多个 本人自己编的 很适合初学者 C语言和matlab的 欢迎下载(Newton numerical procedure used the Adams Jacques ratio of more than 10, I made themselves very suitable for beginners are welcome to download)

the paper describes the existing techniques of face detection.

Knight Online Bot 2014 denemeleri

Cart-pole problem solition with python

自己编写的求解模糊熵程序，eemd分解，并求模糊熵，简单易懂，可实现( solve the fuzzy entropy, easy to understand, can be realized)

SVM在MATLAB下的实现,其中有五个文件夹，分别是不同的SVM(SVM writen by matlab,include five files,and diffent each other)

运动目标检测的程序是有关车辆的，其中涉及到几种方法，有一定的参考价值。(Sports target detection program about the vehicle，reference to several methods and it is worth reading.)

用于通信行业领域，输出一些特定的脉冲波，如连续时间周期三角波、方冲脉波等等(For the communication industries, certain pulse output, such as continuous-time periodic triangular wave, square red pulse)

动态窗口法，直接可以运行的程序。实乃学习路径规划优良demo( Dynamic window method, you can run the program directly. Learning Path planning exercise good demo)