搜索资源列表
MainPre_by_Lya_1
- 混沌时间序列法，该程序用加权一阶局域法对数据进行进行一步预测-Chaotic time series method, the program step forecast data weighted one-rank local law
ChaosToolbox2p9_trial
- 海军工程大学的陆振波博士写的混沌时间序列分析与预测工具箱，版本是2.9，均是matlab源码，个人感觉不错，供做混沌研究的同志们参考。-Chaotic Time Series Analysis and Prediction Matlab Toolbox- version 2.9
pre_by_lya_new
- 基于混沌时间序列的利亚普诺夫指数预测是当今的重要方向，现将自编最新的利亚普诺夫指数预测的分享。-Based on the Liapunov index of chaotic time series prediction is an important direction today, now composing the latest Liapunov index forecast sharing.
short-termloadforecastingwithchaostimeseries
- 文章展示了一种新的方法用于功率系统中短期负载预测。提出的方案使用混沌时间序列分析基于确定性混沌去捕捉复杂的负载行为特征。确定性的混沌允许我们重构一个时间序列并决定输入的变量个数。这篇文章描述了混沌时间序列对日间功率系统峰值的分析。确定性混沌的非线性图形通过多层感知器的神经网络得到。提出的方案在一个例子中具体阐述。-This paper presents a new approach to short-term load forecasting in power systems. The
FS
- 采用局部线性逼近法来预测复杂的混沌时间序列。-Local linear approximation method using chaotic time series prediction
Prediction_RBF
- 混沌时间序列 基于人工神经网络的一步或多步预测-Chaotic time series based on artificial neural network or multi-step prediction step
ChaosToolbox
- 基于MATLAB平台的，混沌时间序列分析与预测工具箱-Chaos Toolbox for time series analisis and forecasting
ChaosAttractors
- 混沌时间序列分析与预测工具箱 (4)同时求时延与嵌入窗的(delay time & embedding window) CC方法 - \C-C Method\Main_CC_Luzhenbo.m (-Chaos time series analysis and forecasting tool box
chaostimeseries
- 实现了各种混沌时间序列模型的设定、估计、检验以及预测等功能-chaos timeseries analysis
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- 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-Prediction is a kind of dynamic filtering, in which past values of one or more time series are used to predict future values. Dynamic neural networks, which include tapped delay lines are used for nonlinear filter
Time-series-prediction-with-anfis
- 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.
choas
- 混沌时间序列分析与预测工具箱 Version1.2-Chaotic time series analysis and prediction toolbox Version1.2
lorenz
- 产生混沌时间序列，常用来测试算法函数逼近及预测能力的测试-Produce chaotic time series
cnn
- 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-Chaos theory and neural network short-term load forecasting, neural network input is essential to choose the program implemented in matlabl choice of embedding dimension based on chaot
1010280956fe8fe2fe0827f49e
- 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-Based on neural network prediction of chaotic time series, including step and multi-step prediction algorithm
fractal-predict-pdf
- 该文章从混沌和分形的关系出发，基于奇异吸引子的分形结构和时间序列的自仿射特性，提出了一种混沌时间序列的预测方法。采用迭代函数系统跟踪混沌的局部运动轨迹，由此确定统计意义上放射性能最优的时间序列段，并分局吸引子定力和拼贴定理建立预测模型。-This article from the chaos and fractal relationship starting, based on the characteristics of self-affine fractal structure and ti
hundunshijianyuce
- 混沌时间序列的Volterra级数多步预测研究，混沌理论认为在混沌系统中，初始条件的十分微小的变化经过不断放大，对其未来状态会造成极其巨大的差别。 -Volterra series of chaotic time series multi-step prediction, chaos theory is that in the chaotic system, the initial conditions of very small changes through continuous zoom,
chaos-time-series-prediction
- 混沌时间序列多步自适应预测方法，非常有用，采用自适应算法，比局部算法要准确-chaos time series prediction
G---P-algorithm-
- G-P算法计算关联维数，可以运用在混沌时间序列的瓦斯浓度预测研究中-GP algorithm to calculate the correlation dimension, you can use the gas concentration in the prediction of chaotic time series in
G_P
- 对于混沌时间序列的预测，重构相空间可用此方法计算嵌入的维数。-it is for the chaotic time series to reconstruct the image space about how to get the dimention number.