work

genetic programming and orthogonal least square

拟合算法：通过matlab的拟合函数实现拟合(Fitting algorithm: fitting function through matlab realize fitting)

surf 特征提取 点特征 尺度不变性 黑森林矩阵(surf feature extraction)

两路语音信号混合及解混的基于自然梯度的极大似然算法MATLAB仿真程序(Two-way voice signals mixed and mixed solution of the natural gradient-based maximum likelihood algorithm of the MATLAB simulation program)

比较系统的QAM radio仿真程序，包含信道编解码，PAM，QPSK等内容，比较全面(Comparison of QAM radio system simulation program, including channel encoding and decoding, PAM, QPSK and other content, more comprehensive)

超声2维成像，运用DAS算法，算法比较简单，可供初学者看看。(2-dimensional ultrasound imaging, the use of DAS algorithm, the algorithm is relatively simple for beginners to see.)

基于Matlab的带通滤波器的设计源码，可以通过此代码，模拟带通滤波器的设计。并附有仿真后的图像。(Matlab-based band-pass filter design source, through this code, analog band-pass filter design. Together with simulation, the image.)

The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order (The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order)

Understanding Star Grounding

matlab中，利用偏最小二乘法优选最佳波长，构建模型，拟合方程(PLSR, a dimensionality reduction method, is emerging as the most robust and reliable chemometric method for constructing models and aims at determining predictor combinations with maximum covariance with the response variable when the measured variables are many and highly collinear)