LS-and-TLS

基于灰色系统方法的离散多目标优化设计程序，很好用(Based on gray system method of discrete multi-objective optimization design process, well used)

是用MATLABA编写的遗传运算发的一个小程序 挺好用的 大家(MATLABA is prepared by the genetic operator issued a small procedure very well, we try to use)

利用Python实现特征的MinMax标准化和Z-Score标准化两种特征归一化方法。并用原特征、两种归一化特征分别进行朴素贝叶斯模型训练，比较模型性能。(Two normalization methods of MinMax normalization and Z-Score standardization are implemented by Python. The naive Bayes model is trained with the original features and two normalization features respectively to compare the performance of the model.)

针对多径效应的影响，提出了一种基于矩阵束的MIMO 雷达低仰角快速估计方法。该方法同时考虑了发射多径信号和接收多径信号，采用单样本数信号矢量构造了一个前后向矩阵束，并利用两个酉矩阵对该矩阵束进行降维处理，最后采用广义特征值分解的总体最小二乘法来估计目标角度。算法不需要估计数据协方差矩阵，可在低 信噪比和单样本数情况下，有效地克服多径效应，实现同时多目标低仰角估计，相比最大似然算法，避免了谱峰搜索，计算量小。仿真结果验证了该算法的有效性。(To overcome the multipath effect, a fast algorithm for low elevation estimation for MIMO radar via matrix pencil is proposed. The signals both in transmit multipath and receive multipath are considered. Firstly, a forward-backward matrix pencil is formed based on the single-sampled vector. Secondly, the reduced-dimensional processing is applied to the matrix pencil via two unitary matrices. Finally, the generalized eigen-decomposition is employed to estimate the low elevation in multipath environments directly. Without the covariance matrix estimation, the proposed method can overcome multipath effect effectively in case of low SNR and estimate multi-target simultaneously. Compared with Maximum Likelihood (ML) method, it avoids spectrum peak searching and reduces the computational burden. Numerical results verify the effectiveness of this method)

计算灰色关联度，包含灰色绝对关联度、相对关联度和归一关联度(Calculate the gray relational grade, contains gray absolute degree relative correlation degree and normalized correlation degree)

计算圆柱绕流的有限体积法计算程序 fortran编写 (Calculating flow around a cylinder)

说明：  分数傅里叶变换程序，这个是二维分数阶傅里叶变化程序。(Fractional Fourier transform procedure, this is a two-dimensional fractional Fourier change process.)

gm模型 gm模型 gm模型 gm模型 gm模型 GM(1,1)(gm model gm model gm model gm model gm model GM (1,1))

许多图像问题需要某种物体的检测，其中图像之间的物体的外观有自然的变化。 如人脸识别，病变检测，神经通道分割。 这些图像问题可以通过手动注释图像对象来解决，以训练识别正常物体外观的模型。 这可以通过基于PCA的最大似然分类器来完成。(PCA algorithm suitable for detection / recognition of 2D image "objects")

粉土的阻尼比拟合程序。拟合模型选择Davidenkov模型。(The silt damping match the proper procedure. The Fitting Model Davidenkov model.)