This paper proposes an improved adding-weight one-rank local-region method for prediction of chaotic time series.
提出了一种用于混沌时间序列预测的改进型加权一阶局域法。
The prediction method of weight local basis function is presented based on the deep research on local prediction for chaotic time series.
在深入研究混沌时间序列局域预测方法的基础上，提出了一种加权局域基函数预测方法。
Based on local linear prediction model of chaotic time series, short-term load forecasting method on multi-embedding dimension is presented.
基于混沌时间序列的局域线性预测模型，提出了多嵌入维的短期负荷预测方法。
In the second part, the chaotic prediction models for hydrological time series are studied in terms of the chaotic characteristics of hydrological evolution process.
第二部分基于水文序列变化的混沌特性，对水文时间序列的混沌预测模型进行了研究。
Based on this, the average predictable size and the longest predictable size of chaotic time series are provided in this paper to definite the time range of short-term prediction.
基于此，给出了混沌时间序列的平均可预测尺度及最长可预测尺度，以此来界定短期预测的时间范围。
A method of chaotic time series prediction problem based on local dynamical similarity is proposed.
基于混沌系统局部特征，提出了一种局部动力相似的混沌时间序列的预测方法。
Aiming at the issue about multi-step prediction of the traffic flow chaotic time series, a fast learning algorithm of wavelet neural network (WNN) based on chaotic mechanism is proposed.
针对交通流量混沌时间序列多步预测的问题，提出了一种基于混沌机理的小波神经网络(WNN)快速学习算法。
Thus, the chaotic analytic method is set up for the prediction of the measured displacement time series of rock mass engineering.
据此，建立了岩体工程位移观测数据的混沌预测方法。
Secondly the prediction technology of chaotic time series is studied based on memory-based predictor.
分析了基于记忆库混沌时间序列预测方法，引入一种改进核函数的支持向量机分类器。
In this paper, the traditional echo state network (ESN) through the structure and learning mechanism of the study, on the echo state network prediction method of chaotic time series.
本文主要通过对传统回声状态网络(esn)的结构和学习机理的研究，探讨了回声状态网络对混沌时间序列的预测方法。
Using the ability of short-term predicting for chaotic time series, the paper constructed a gas concentration prediction model for certain coal mines.
利用混沌时间序列短期可以预测的特点，对选取的某两处煤矿构建了瓦斯浓度预测模型。
As a new type of recurrent neural network, echo state network (ESN) is applied to nonlinear system identification and chaotic time series prediction.
ESN(回声状态网络)是一种新型的递归神经网络，可有效处理非线性系统辨识以及混沌时间序列预测问题。
As a new type of recurrent neural network, echo state network (ESN) is applied to nonlinear system identification and chaotic time series prediction.
ESN(回声状态网络)是一种新型的递归神经网络，可有效处理非线性系统辨识以及混沌时间序列预测问题。
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