Description: 小数据量法求混沌吸引子最大Lyapunov指数的Matlab程序，参考文献：张家树.混沌时间序列的Volterra自适应预测.物理学报.2000.03-small data method for chaotic attractor largest Lyapunov exponent of Matlab procedures References : Zhang Shu. The chaotic time series Volterra adaptive prediction. Physics reported .2000.03 Platform: |
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Author:江维 |
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Description: 基于局域法多步预报模型的混沌时间序列预报模型，对多个典型混沌序列的仿真测试表明，本算法具有良好的多步预测精度和较好的抗噪声能力-based multi-step prediction model of chaotic time series prediction model, a number of typical chaotic sequence of simulation tests show that the algorithm has a good multi-step forecast accuracy and better noise immunity Platform: |
Size: 227328 |
Author:蔡烽 |
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Description: 小数据量法求混沌吸引子最大Lyapunov指数的Matlab程序，参考文献：张家树.混沌时间序列的Volterra自适应预测.物理学报.2000.03-small data method for chaotic attractor largest Lyapunov exponent of Matlab procedures References : Zhang Shu. The chaotic time series Volterra adaptive prediction. Physics reported .2000.03 Platform: |
Size: 8192 |
Author:江维 |
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Description: 混沌时间序列局域法多步预报模型.doc(有程序下载)
针对混沌时间序列预测中用加权一阶局域法单步预报模型进行多步预报时计算量大且存在误差累积效应的不足，本文提出了基于相空间重构技术的局域法多步预报模型，包括加权一阶局域法多步预报模型和RBF神经网络多步预报模型。对几种典型混沌序列的预测仿真表明，两种模型对混沌时间序列的多步预报均较有效。
-chaotic time series Local Law multi-step prediction model. Doc (with the download) against chaotic time series prediction using a weighted-Local law single-step prediction model multi-step forecast at large calculation error and the cumulative effect of the shortage, In this paper, based on the phase-space reconstruction of local law multi-step prediction model Weighted including a local law-order multi-step prediction model and RBFNN multi-step prediction model. Several typical of the chaotic sequence forecast simulation shows that the two models of chaotic time series multi-step prediction than effective. Platform: |
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Author:呆雁 |
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Description: 采用自相关法计算混沌时间序列最小延迟时间的Matlab原程序。-using autocorrelation calculated chaotic time series smallest delay time of the original program Matlab. Platform: |
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Author:baozi9 |
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Description: 互信息matlab, 混沌时间序列分析与预测工具箱 Version1.0
点这里下载: 混沌时间序列分析与预测工具箱 Version1.0
chaotic time series analysis and prediction matlab toolbox - version 1.0
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
-Mutual information matlab, chaotic time series analysis and forecast Version1.0 point toolbox here to download: Chaotic Time Series Analysis and Forecast of the toolbox Version1.0chaotic time series analysis and prediction matlab toolbox- version 1.01, the kit includes a chaotic time series analysis with the projected common method, there are Platform: |
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Author:fdsfsfsf |
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Description: 资源分配神经网络解决Mackey-Glass时间序列预测函数逼近问题-Neural network to solve the allocation of resources Mackey-Glass time series prediction function approximation problem Platform: |
Size: 1024 |
Author:吴强 |
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Description: 混沌时间序列分析与预测工具箱，包括了混沌时间序列分析的很多方法和预测方法。-Chaotic time series analysis and prediction toolbox, including the analysis of chaotic time series prediction in many ways and methods. Platform: |
Size: 467968 |
Author:111111 |
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Description: matlab编写的基于混沌时间序列的神经网络预测，包括一步和多步预测算法。-matlab prepared chaotic time series based on the neural network to predict, including step and multi-step prediction algorithm. Platform: |
Size: 9216 |
Author:jcuaon |
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Description: 多变量混沌时间序列预测及其在股票市场中的应用 硕士论文-Multivariate Chaotic Time Series Prediction and Its Application in the stock market in the master' s thesis Platform: |
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Author:周寒 |
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Description: 为了选择神经网络的最好结构以及增强模型的推广能力，提出一种自适应支持向量回归神经网络(SVR—NN)。SVR—NN 用支持向量回归(SVR)方法获得网络的初始结构和权值， 白适应地生
成网络隐层结点，然后用基于退火过程的鲁棒学习算法更新网络结点疹教和权 主。 SVR—NN有很
好的收敛性和鲁棒性，能抑制由于数据异常和参数选择不当所导致的“过拟合，’现象。将SVR—NN
应用到时间序列预测上。结果表明，SVR．NN预测模型能精确地预测混沌时间序列，具有很好的
理论和应用价值。-Abstract：To select the‘best’structure of the neural networks and enhance the generalization ability of models．a support
vector regression neural networks fSVR-NN)was proposed．Firstly，support vector regression approach was applied to
determine initial structure and initial weights of SVR．NN SO that the number of hidden layer nodes can be constructed
adaptively based on support vectors．Furthermore，an annealing robust learning algorithm was further presented to fine
tune the hidden node parameters and weights of SVR一ⅣM The adaptive SVR．NN has faSt convergence speed and robust
capability．and it can also suppress the ‘orerfitting’phenomena when the train data ncludes outliers．The adaptive
SVR．NN was then applied to time series prediction．Experimental results show that the adaptive SVR．ⅣⅣ can accurately
predict chaotic time series，and it iS valuable in both theory and application aspects． Platform: |
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Author:11 |
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Description: This simulink model simulates the damped driven pendulum, showing it s chaotic motion.
theta = angle of pendulum
omega = (d/dt)theta = angular velocity
Gamma(t) = gcos(phi) = Force
omega_d = (d/dt) phi
Gamma(t) = (d/dt)omega + omega/Q + sin(theta)
Play with the initial conditions (omega_0, theta_0, phi_0 = omega(t=0), theta(t=0), phi(t=0)) and the system parameters (g, Q, omega_d) and the solver parameters/method.
Chaos can be seen for Q=2, omega_d=w/3.
The program outputs to Matlab time, theta(time) & omega(time).
Plot the phase space via:
plot(mod(theta+pi, 2*pi)-pi, omega, . )
Plot the Poincare sections using:
t_P = (0:2*pi/omega_d:max(time))
plot(mod(spline(time, theta+pi, t_P), 2*pi)-pi, spline(time, omega, t_P), . )
System is described in:
"Fractal basin boundaries and intermittency in the driven damped pendulum"
E. G. Gwinn and R. M. Westervelt
PRA 33(6):4143 (1986)
-This simulink model simulates the damped driven pendulum, showing it s chaotic motion.
theta = angle of pendulum
omega = (d/dt)theta = angular velocity
Gamma(t) = gcos(phi) = Force
omega_d = (d/dt) phi
Gamma(t) = (d/dt)omega+ omega/Q+ sin(theta)
Play with the initial conditions (omega_0, theta_0, phi_0 = omega(t=0), theta(t=0), phi(t=0)) and the system parameters (g, Q, omega_d) and the solver parameters/method.
Chaos can be seen for Q=2, omega_d=w/3.
The program outputs to Matlab time, theta(time) & omega(time).
Plot the phase space via:
plot(mod(theta+pi, 2*pi)-pi, omega, . )
Plot the Poincare sections using:
t_P = (0:2*pi/omega_d:max(time))
plot(mod(spline(time, theta+pi, t_P), 2*pi)-pi, spline(time, omega, t_P), . )
System is described in:
"Fractal basin boundaries and intermittency in the driven damped pendulum"
E. G. Gwinn and R. M. Westervelt
PRA 33(6):4143 (1986)
Platform: |
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Author:Mike Gao |
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Description: 经典书籍--混沌时间序列分析及其应用-作者吕金虎-Classic books- Chaos time series analysis and its applications- the author Lvjin Hu Platform: |
Size: 4182016 |
Author:鲍晓 |
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Description: 详细介绍了混沌时间序列的分析及应用,并给出了具体的例子。-describing the applycation of chaotic time series and giving some examples. Platform: |
Size: 3865600 |
Author:胡增运 |
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Description: 采用模糊神经网络anfis预测混沌时间序列的源程序。-The source program of Using fuzzy ANFIS neural network for predicting chaotic time series. Platform: |
Size: 571392 |
Author:胡玉霞 |
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Description: 针对采用回声状态网络预测多元混沌时间序列时存在的病态解问题 , 本文建立了因子回声状态网络模型 , 通过因子分析 (Factor analysis, FA) 方法提取高维储备池状态矩阵的公因子 , 去除冗余和噪声成分 .-When an echo state network is used to predict multivariate time series, there may exist ill-posed problem. This pa-
per proposes a novel prediction model, named factor echo state
network, to solve the problem. It uses a factor analysis (FA) algorithm to extract the common factors of the reservoir matrix,and to remove the redundancies and noises. Platform: |
Size: 770048 |
Author:mafeng |
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Description: 混沌时间序列Matlab源程序,包含时间序列的时间延迟计算，关联积分计算，相空间重构，时间序列分解，Heaviside函数的计算，延迟时间和时间窗口计算，混沌吸引子关联维计算，重构相空间进行K_L变换，混沌吸引子关联维计算，Hurst指数分析，关联维和Kolmogorov熵计算，FFT计算序列平均周期，最大lyapunov指数计算，利用互信息法求时间延迟，混沌和噪声识别的源程序。-Matlab chaotic time series source, time includes the time series of delay calculation, correlation integral calculation, phase space reconstruction, time series decomposition, calculated Heaviside function, the delay time and the time window calculated correlation dimension of chaotic attractors calculated reconstruction phase space K_L transform computing correlation dimension of chaotic attractors, Hurst exponent analysis, correlation dimension and Kolmogorov entropy calculation, FFT calculation sequence averaging period, maximum lyapunov index, mutual information method the time delay, chaos and noise source identification. Platform: |
Size: 26624 |
Author:马喆 |
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Search - the Chaotic Time-Series