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1、该工具箱包括了混沌时间序列分析与预测的常用方法,有: (1)产生混沌时间序列(chaotic time series) Logistic映射 - \ChaosAttractors\Main_Logistic.m Henon映射 - \ChaosAttractors\Main_Henon.m Lorenz吸引子 - \ChaosAttractors\Main_Lorenz.m Duffing吸引子 - \ChaosAttractors\Main_Duffing.m Duffing2吸引子 - \ChaosAttractors\Main_Duffing2.m Rossler吸引子 - \ChaosAttractors\Main_Rossler.m Chens吸引子 - \ChaosAttractors\Main_Chens.m Ikeda吸引子 - \ChaosAttractors\Main_Ikeda.m MackeyGLass序列 - \ChaosAttractors\Main_MackeyGLass.m Quadratic序列 - \ChaosAttractors\Main_Quadratic.m (2)求时延(delay time) 自相关法 - \DelayTime_Others\Main_AutoCorrelation.m 平均位移法 - \DelayTime_Others\Main_AverageDisplacement.m (去偏)复自相关法 - \DelayTime_Others\Main_ComplexAutoCorrelation.m 互信息法 - \DelayTime_MutualInformation\Main_Mutual_Information.m (3)求嵌入维(embedding dimension) 假近邻法 - \EmbeddingDimension_FNN\Main_FNN.m Cao方法 - \EmbeddingDimension_Cao\Main_EmbeddingDimension_Cao.m (4)同时求时延与嵌入窗(delay time & embedding window) CC方法 - \C-C Method\Main_CC_Luzhenbo.m (5)求关联维(correlation dimension) GP算法 - \CorrelationDimension_GP\Main_CorrelationDimension_GP.m (6)求K熵(Kolmogorov Entropy) GP算法 - \KolmogorovEntropy_GP\Main_KolmogorovEntropy_GP.m STB算法 - \KolmogorovEntropy_STB\Main_KolmogorovEntropy_STB.m (7)求最大Lyapunov指数(largest Lyapunov exponent) 小数据量法 - \LargestLyapunov_Rosenstein\Main_LargestLyapunov_Rosenstein1.m \LargestLyapunov_Rosenstein\Main_LargestLyapunov_Rosenstein2.m \LargestLyapunov_Rosenstein\Main_LargestLyapunov_Rosenstein3.m \LargestLyapunov_Rosenstein\Main_LargestLyapunov_Rosenstein4.m (8)求Lyapunov指数谱(Lyapunov exponent spectrum) BBA算法 - \LyapunovSpectrum_BBA\Main_LyapunovSpectrum_BBA1.m \LyapunovSpectrum_BBA\Main_LyapunovSpectrum_BBA2.m (9)求二进制图形的盒子维(box dimension)和广义维(genealized dimension) 覆盖法 - \BoxDimension_2D\Main_BoxDimension_2D.m \GeneralizedDimension_2D\Main_GeneralizedDimension_2D.m (10)求时间序列的盒子维(box dimension)和广义维(genealized dimension) 覆盖法 - \BoxDimension_TS\Main_BoxDimension_TS.m \GeneralizedDimension_TS\Main_GeneralizedDimension_TS.m (11)混沌时间序列预测(chaotic time series prediction) RBF神经网络一步预测 - \Prediction_RBF\Main_RBF.m RBF神经网络多步预测 - \Prediction_RBF\Main_RBF_MultiStepPred.m Volterra级数一步预测 - \Prediction_Volterra\Main_Volterra.m Volterra级数多步预测 - \Prediction_Volterra\Main_Volterra_MultiStepPred.m (12)产生替代数据(Surrogate Data) 随机相位法 - \SurrogateData\Main_SurrogateData.m 2、在matlab环境中首先运行install.m,将工具箱所在路径添加至matlab 3、各子目录下以Main_开头的文件即是主程序文件,直接按快捷键F5运行即可 4、工具箱中所有程序均在Matlab6.5和Matlab7.1环境中调试通过,不能保证在Matlab其它版本正确运行。 5、工具箱中部分功能为试用版,敬请谅解! 6、 作者:陆振波,海军工程大学 欢迎同行来信交流与合作,更多文章与程序下载请访问我的个人主页
Date : 2009-03-12 Size : 566.38kb User : niuchao0511

entropy program that is a data for a test for a value of entropy
Date : 2025-12-26 Size : 4kb User : Aziz rwhani

基于信息熵的蚁群聚类JAVA实现。包含测试数据。-Ant Colony Clustering JAVA based on information entropy. Contains the test data.
Date : 2025-12-26 Size : 59kb User : justok

下载MNIST数据集(手写体数字0-9)后,搭建卷积神经网络,将输入的数据集经过一层一层的卷积,到最后计算交叉熵,用梯度下降算法去优化它,使它变得最小,这就训练出了权重和偏置量,识别的准确率为91%(Download the MNIST data set (handwritten number 0-9), build a convolutional neural network, the input data set by convolutional layers, finally calculate the cross entropy with the gradient descent algorithm to optimize it, so that it becomes the minimum, this training weight and bias, recognition accuracy rate 91%)
Date : 2025-12-26 Size : 11.06mb User : 未来已来

自己做的一些层次聚类 有一些数据集 和一些仿真图 用的是信号熵特征进行聚类(Some of the hierarchical clustering done by myself, some data sets and some simulation maps are clustered by means of signal entropy features.)
Date : 2025-12-26 Size : 1.26mb User : 怪盗基德040

特色:1.借用小波包分解和小波能量熵函数;2.GUI界面导入西储大学轴承故障数据;3:对提取小波能量方便快捷(Features: 1. Use wavelet packet decomposition and wavelet energy entropy function; 2. GUI interface to import bearing fault data of Xichu University; 3. It is convenient and fast to extract wavelet energy)
Date : 2025-12-26 Size : 7.12mb User : lynn2017
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