importAgilentBin

说明：  在Visual C++中调用matlab中定义的函数，可以方便利用matlab的资源(Calling Matlab defined in the function, Matlab can facilitate the use of resources)

largrangian method largrangian method largrangian methodlargrangian method

说明：  基于主动轮廓模型的虹膜图像定位算法研究及其matlab实现(Iris recognization, Matlab, based on snake,actice countours)

SEÑ AL DIENTE SIERRA GUI

matlab牛顿法的函数和执行调用的结果(Newton method functionand the results )

The Frequency Domain Decomposition (FDD) is an output-only system identification technique popular in civil engineering, in particular in structural health monitoring. As an output-only algorithm, it is useful when the input data is unknown. FDD is a modal analysis technique which generates a system realization using the frequency response given (multi-)output data

使用BP神经网络训练数据并且作出预测，比较预测值与真实值的误差。(The use of BP neural network training data and make predictions, comparing predicted and actual values of error.)

使用基于峭度的FASTICA盲分离算法，实现对亚高斯信号和超高斯混合信号的分离，并从分离结果可总结出盲分离算法的两个不确定性。(FASTICA kurtosis-based blind source separation algorithm to achieve sub-Gaussian and super-Gaussian mixed-signal separation, the separation results from the separation algorithm can be summarized in two uncertainties.)

2. 设 是窄带信号，定义 是在 区间上均匀分布的随机相位。 是寬带信号，它是一个零均值、方差为1的白噪音信号e(n)激励一个线性滤波器而产生，其差分方程为 。 1） 计算 和 各自的自相关函数，并画出其函数图形。根据此选择合适的延时，以实现谱线增强。 2） 产生一个 序列。选择合适的 值。让 通过谱线增强器。画出输出信号 和误差信号e(n)的波形，并分别与 和 比较。 (Computer Experiments: 1. Consider an AR process x(n) defined by the difference equation where v(n) is an additive white noise of zero mean and variance .The AR parameters and are both real valued: a) Calculate the noise variance such that the AR process x(n) has unit variance .Hence , generate different realization of the process x (n). b) Given the input x (n), an LMS filter of length M = 2 is used to estimate the unknown AR parameters and . The step size is assigned the value 0.05. Compute and plot the ensemble average curve of and by averaging the value of parameters and over an ensemble of 100 different realization of the filter. Calculate the time constant according to the experiment results and compare with the corresponded theoretical value. c) For one realization of the LMS filter, compute the prediction error And the two tap-weight errors and Using power spectral plots of , show that)

computer manual for Matlab in pattern classification