relay_superimposed

说明：  基于石油钻井神经网络PID仿真器模型，该模型验证了该算法的可行性和有效性(BTF simulink fangzhen)

说明：  simulink 仿真锁相环的一个pdf(a pdf of pll using simulink )

BP辨识，，，，，，，，，，，，，，，，，，，，，(bp identify，，，，，，，，，，，，，，，，， )

硕士学位论文基于无线传感器网络的水下目标识别中的线谱特征提取，(Master' s degree thesis on wireless sensor networks for underwater target recognition in the line spectrum feature extraction,)

两倍过采样的频域均衡器，采用MMSE算法最小化均方误差(two upsample frequency decision feedback equaliter，with MMSE criterion)

融合重力梯度和磁场数据的使用变形分析异常的方法。(Fusion of gravity gradient and magnetic field data for discrimination of anomalies using deformation analysis)

Matlab Tips and tricks

说明：  数学建模1998年A题论文集。其中有matlab中的程序的思路及计算的结果。非常好，很难得的。(Mathematical Modeling A title in 1998 collection. Matlab in which the idea of the procedures and calculation results. Very good, hard to come by.)

永磁同步电机直接转矩控制simulink仿真模型(Simlink simulation model of direct torque control for permanent magnet synchronous motor)

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))