▍1. Closest-bifurcation-MATPOWER-master
寻求最近的平衡点方程，属于非线性动力学理论。希望对大家有帮助(Seeking the nearest equilibrium equation belongs to the theory of non-linear dynamics. I hope it will be helpful to you all.)
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▍2. Closest-bifurcation-MATPOWER-master
说明： 寻求最近的平衡点方程，属于非线性动力学理论。希望对大家有帮助(Seeking the nearest equilibrium equation belongs to the theory of non-linear dynamics. I hope it will be helpful to you all.)
▍3. DMC_contol
说明： 用于实现任意模型的DMC预测，并加入校正模型(DMC prediction for arbitrary models and incorporation of correction models)
▍4. 拉普拉斯金字塔
实现红外与可见光图像融合， 简单拉普拉斯方法(A Simple Laplace Method for Fusion of Infrared and Visible Image)
▍5. 拉普拉斯金字塔
说明： 实现红外与可见光图像融合， 简单拉普拉斯方法(A Simple Laplace Method for Fusion of Infrared and Visible Image)
▍6. ekfukf
Kalman滤波器的Matlab工具箱，实现了线性Kalman，EKF，UKF，CKF和IMM滤波器(The matlab toolbox of Kalman filter implements linear Kalman, EKF, UKF, CKF and IMM filters)
▍7. ekfukf
说明： Kalman滤波器的Matlab工具箱，实现了线性Kalman，EKF，UKF，CKF和IMM滤波器(The matlab toolbox of Kalman filter implements linear Kalman, EKF, UKF, CKF and IMM filters)
▍11. Copula理论及应用实例
Copula理论及应用实例，主要是用函数计算两个相关变量的相关性，并生产相关性样本(Copula theory and application examples, mainly use function to calculate the correlation of two related variables, and produce correlation samples.)
▍12. Copula理论及应用实例
说明： Copula理论及应用实例，主要是用函数计算两个相关变量的相关性，并生产相关性样本(Copula theory and application examples, mainly use function to calculate the correlation of two related variables, and produce correlation samples.)
▍14. wavelet
能对采集到的信号进行小波分解重构，从而进行进一步的分析(The collected signal can be decomposed and reconstructed by wavelet transform, so as to make further analysis.)
▍15. wavelet
说明： 能对采集到的信号进行小波分解重构，从而进行进一步的分析(The collected signal can be decomposed and reconstructed by wavelet transform, so as to make further analysis.)
▍16. chapter1
每组数据为25维，第1维维类别标识，后24维为语音特征信号。然后把四类语音信号何为一组，抽取1500组数据作为训练数据，500组数据作为测试数据，并对数据进行归一化处理。根据语音类别标识设定每组语音信号的期望输出值，如标识类为1时，期望输出向量为[1 0 0 0]。(Each group of data has 25 dimensions, the first dimension is identified by category, and the last 24 dimensions are speech feature signals.Then, select one group of four speech signals, extract 1500 groups of data as training data and 500 groups of data as test data, and normalize the data.Set the expected output value of each group of speech signals according to the speech category identification. For example, when the identification class is 1, the expected output vector is [1 0 0 0].)
▍17. chapter1
说明： 每组数据为25维，第1维维类别标识，后24维为语音特征信号。然后把四类语音信号何为一组，抽取1500组数据作为训练数据，500组数据作为测试数据，并对数据进行归一化处理。根据语音类别标识设定每组语音信号的期望输出值，如标识类为1时，期望输出向量为[1 0 0 0]。(Each group of data has 25 dimensions, the first dimension is identified by category, and the last 24 dimensions are speech feature signals.Then, select one group of four speech signals, extract 1500 groups of data as training data and 500 groups of data as test data, and normalize the data.Set the expected output value of each group of speech signals according to the speech category identification. For example, when the identification class is 1, the expected output vector is [1 0 0 0].)
▍18. 基于形态学滤波去噪matlab代码
说明： 基于形态学滤波去噪，可以运行的程序。。。。(morphological filters)
▍19. 有限质点法进行索(膜)找形的MATLAB程序
用于有限元索膜结构在matlab实现找形分析的主函数(Main function for finite element cable-membrane structure to realize shape-finding analysis in matlab)
▍20. 有限质点法进行索(膜)找形的MATLAB程序
说明： 用于有限元索膜结构在matlab实现找形分析的主函数(Main function for finite element cable-membrane structure to realize shape-finding analysis in matlab)
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