Description: 假近邻法(False Nearest Neighbor, FNN)计算嵌入维的Matlab程序
文件夹说明：
Main_FNN.m - 程序主函数，直接运行此文件即可
LorenzData.dll - 产生Lorenz时间序列
PhaSpaRecon.m - 相空间重构
fnn_luzhenbo.dll - 假近邻计算主函数
SearchNN.dll - 近邻点搜索
buffer_SearchNN_1.dll - 近邻点搜索缓存1
buffer_SearchNN_2.dll - 近邻点搜索缓存2
参考文献：
M.B.Kennel, R.Brown, H.D.I.Abarbanel. Determining embedding dimension for phase-space reconstruction using a geometrical construction[J]. Phys. Rev. A 1992,45:3403.
-false neighbors (False Nearest Neighbor, FNN) calculation embedding dimension of the Matlab program folder : Main_FNN.m-procedure main function, Direct operating this document can be LorenzData.dll-time series produced Lorenz PhaS paRecon.m-phase space reconstruction fnn_luzhenbo.dll-calculated at the main function neighbors SearchNN.dll-point search buffer_SearchNN_1.dll neighbor - Search neighbor point a buffer_SearchNN_2.dll Cache-Cache Search neighbors point two reference Literature : M. B. Kennel, R. Brown, H. D. I. Abarbanel. Determining embedding dime nsion for phase-space reconstruction using a g eometrical construction [J]. Phys. Rev. A 1992 , 45:3403. Platform: |
Size: 100029 |
Author:呆雁 |
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Description: 在用混沌理论和神经网络进行短期负荷预测时，神经网络的输入的选择至关重要，该程序用matlabl实现了基于混沌时间序列的嵌入维数的选择-using chaos theory and neural networks for short-term load forecasts, the neural network is essential to choose an input, The procedure used matlabl achieved a chaotic time series based on the embedding dimension of choice Platform: |
Size: 1227 |
Author:sunyan |
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Description: Determining embedding dimension for phase-space reconstruction using a geometrical construction.
It is very important reference for time forecast in chaos sequence.
Platform: |
Size: 1567795 |
Author:赵河 |
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Description: 关于混沌Tisean3.0嵌入维的计算程序，希望对大家有用-Chaos Tisean3.0 embedding dimension on the calculation procedure, in the hope that useful to everybody Platform: |
Size: 76800 |
Author:yy |
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Description: Determining embedding dimension for phase-space reconstruction using a geometrical construction.
It is very important reference for time forecast in chaos sequence.
-Determining embedding dimension for phase-space reconstruction using a geometrical construction.It is very important reference for time forecast in chaos sequence. Platform: |
Size: 1567744 |
Author:赵河 |
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Description: 计算嵌入维程序，可以应用于很多的方面-Embedding dimension calculation procedure can be applied to many aspects of Platform: |
Size: 23552 |
Author:neng |
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Description: 在相空间重构中，用互信息法求最小关联嵌入维-In phase space reconstruction, the use of mutual information method associated minimum embedding dimension Platform: |
Size: 4096 |
Author:刘洋 |
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Description: 混沌时间序列预测中用gp算法求借嵌入维和分形维。-Chaotic time series prediction by using gp algorithm for embedding dimension and fractal dimension. Platform: |
Size: 3072 |
Author:章楷桦 |
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Description: 混沌时间序列分析与预测源代码。具有产生混沌时间序列，求时延，求嵌入维，求关联维，求K熵，求Lyapunov指数谱，求二进制图形的盒子维和广义维，求时间序列的盒子维和广义维，混沌时间序列预测等项功能。-Chaotic time series analysis and prediction of the source code. Has generated chaotic time series, and delay, and embedding dimension, and correlation dimension, and K-entropy, and Lyapunov exponent spectra, and the binary graphics box peacekeeping generalized dimensions, and time series of box-dimensional and generalized dimension, chaotic time series prediction functions. Platform: |
Size: 579584 |
Author:李志 |
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Description: GP算法计算时间序列想空间重构的最佳嵌入维数和延迟时间-GP algorithm space reconstruction time series like the best embedding dimension and delay time Platform: |
Size: 1024 |
Author:张艳艳 |
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Description: 混沌工具箱
C-C方法计算时间延迟和嵌入维数
混沌时间序列预测-Chaos Toolbox CC method to calculate time delay and embedding dimension of chaotic time series prediction Platform: |
Size: 14336 |
Author:潘水洋 |
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Description: 混沌时间序列分析中的Ｇ－Ｐ方法，用来计算相空间重构用到的关联维和嵌入维数-Chaotic time series analysis of the G-P method, used to calculate the phase-space reconstruction using the correlation dimension and embedding dimension Platform: |
Size: 1024 |
Author:lishuhui |
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Description: 应用改进的CC法求取时间延迟和嵌入维数，效果不错-The modified CC method to strike a time delay and embedding dimension, good results Platform: |
Size: 2048 |
Author:NOAH CHAN |
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Description: C-C算法应用关联积分能够同时估计出时间延迟和嵌入维数，是相空间重构的前提。
本程序通过C-C算法计算duffing方程产生的混沌时间序列的时间延迟和嵌入维数。-CC algorithm is used to simultaneously estimate the correlation integral time delay and embedding dimension, is a prerequisite for phase space reconstruction.
This procedure CC algorithm duffing equation chaotic time series generated by the time delay and embedding dimension. Platform: |
Size: 48128 |
Author: |
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Description: 嵌入维数自适应最小二乘支持向量机
状态时间序列预测方法
Condition Time Series Prediction Using Least Squares Support Vector Machine
with Adaptive Embedding Dimension
针对航空发动机状态时间序列预测中嵌入维数难于有效选取的问题, 提出一种基于嵌入维数自适应
最小二乘支持向量机( L SSVM ) 的预测方法。该方法将嵌入维数作为影响状态时间序列预测精度的重要参
数, 以交叉验证误差为评价准则, 利用粒子群优化( P SO ) 进化搜索LSSV M 预测模型的最优超参数与嵌入维
数, 同时通过矩阵变换原理提高交叉验证过程的计算效率, 并最终建立优化后的L SSVM 预测模型。航空发
动机排气温度( EGT ) 预测实例表明, 该方法可自适应选取适用于状态时间序列预测的最优嵌入维数且预测
精度高, 适用于航空发动机状态时间序列预测。- T o deal wit h the difficulty of selecting an appro pr iate embedding dimension for aeroeng ine co ndition
time series predictio n, a metho d based o n least squar es suppo rt vecto r machine ( L SSVM ) with ada ptive em
bedding dimension is pro po sed. I n the method, the embedding dimensio n is identified as a parameter that af
fects the accuracy o f the aer oengine condition time series predictio n par ticle sw arm o ptimizat ion ( P SO) is ap
plied to optimize the hyperpar ameter s and embedding dimension of the L SSV M pr edict ion model cro ssv alida
tion is applied to evaluate the perfo rmance o f the L SSVM predictio n mo del and matr ix tr ansfo rm is applied to
the L SSVM pr ediction model tr aining to accelerate the crossvalidation evaluation pro cess. Ex periments on an
aeroengine ex haust g as t emperatur e ( EGT ) predictio n demonst rates that the metho d is hig hly effective in em
bedding dimension selection. In compar ison w ith co nv Platform: |
Size: 342016 |
Author: |
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Description: cao算法，可以确定嵌入维数，从而实现相空间重构-Cao algorithm, embedding dimension of time series can be determined, so as to realize the phase space reconstruction Platform: |
Size: 64512 |
Author:黄刘军 |
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Search - embedding dimension