检索结果-南通市图书馆

CNN-LSTM based incremental attention mechanism enabled phase-space reconstruction for chaotic time series prediction

引用

Journal of Electronic Science and Technology 2024年第2期22卷 77-90页

作者： Xiao-Qian Lu Jun Tian Qiang Liao Zheng-Wu Xu Lu Gan Bidding and Purchasing Center University of Electronic Science and Technology of ChinaChengdu611731China Yibin Research Institute University of Electronic Science and Technology of ChinaYibin644000China School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China

To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network,we propose a convolutional neural network-long short-term memory(CNN-LSTM)prediction model based on the incremental attention ***,a traversal search is conducted through the traversal layer for finite parameters in the phase ***,an incremental attention layer is utilized for parameter judgment based on the dimension weight criteria(DWC).The phase space parameters that best meet DWC are selected and fed into the input ***,the constructed CNN-LSTM network extracts spatio-temporal features and provides the final prediction *** model is verified using Logistic,Lorenz,and sunspot chaotic time series,and the performance is compared from the two dimensions of prediction accuracy and network phase space ***,the CNN-LSTM network based on incremental attention is compared with long short-term memory(LSTM),convolutional neural network(CNN),recurrent neural network(RNN),and support vector regression(SVR)for prediction *** experiment results indicate that the proposed composite network model possesses enhanced capability in extracting temporal features and achieves higher prediction ***,the algorithm to estimate the phase space parameter is compared with the traditional CAO,false nearest neighbor,and C-C,three typical methods for determining the chaotic phase space *** experiments reveal that the phase space parameter estimation algorithm based on the incremental attention mechanism is superior in prediction accuracy compared with the traditional phase space reconstruction method in five networks,including CNN-LSTM,LSTM,CNN,RNN,and SVR.

关键词： chaotic time series Incremental attention mechanism Phase-space reconstruction

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Prediction and analysis of chaotic time series on the basis of support vector

引用

Journal of Systems Engineering and Electronics 2008年第4期19卷 806-811页

作者： Li Tianliang He Liming Li Haipeng The Engineering Inst. Air Force Engineering Univ. Xi'an 710038 P. R. China

Based on discussion on the theories of support vector machines （SVM）, an one-step prediction model for time series prediction is presented, wherein the chaos theory is incorporated. chaotic character of the time series is taken into account in the prediction procedure; parameters of reconstruction-detay and embedding-dimension for phase-space reconstruction are calculated in light of mutual-information and false-nearest-neighbor method, respectively. Precision and functionality have been demonstrated by the experimental results on the basis of the prediction of Lorenz chaotic time series.

关键词： support vector machines chaotic time series prediction model functionality

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

A method to improve the precision of chaotic time series prediction by using a non-trajectory

引用

Chinese Physics B 2009年第8期18卷 3287-3294页

作者：闫华魏平肖先赐 School of Electronic Engineering University of Electronic Science and Technology of China

Due to the error in the measured value of the initial state and the sensitive dependence on initial conditions of chaotic dynamical systems, the error of chaotic time series prediction increases with the prediction step. This paper provides a method to improve the prediction precision by adjusting the predicted value in the course of iteration according to the evolution information of small intervals on the left and right sides of the predicted value. The adjusted predicted result is a non-trajectory which can provide a better prediction performance than the usual result based on the trajectory. Numerical simulations of two typical chaotic maps demonstrate its effectiveness. When the prediction step gets relatively larger, the effect is more pronounced.

关键词： non-trajectory chaotic time series prediction

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Stabilization of chaotic time series by Proportional Pulse in the System Variable Based on Genetic Algorithm

引用

International Journal of Minerals,Metallurgy and Materials 1999年第3期13卷 228-229页

作者： Qing Li Deling Zheng Jianlong Zhou(Information Engineering School, University of Science and Technology Beijing, Beijing 100083, China)(Handan iron and Steel Co., Handan 056015, China) Information Engineering School University of Science and Technology Beijing Beijing 100083 China Handan Iron and Steel Co. Handan 056 015 China

The PPSV (Proportional Pulse in the System Variable) algorithm is a convenient method for the stabilization of the chaotic time series. It does not require any previous knowledge of the system. The PPSV method also has a shortcoming, that is, the determination off. is a procedure by trial and error, since it lacks of optimization. In order to overcome the blindness, GA (Genetic Algorithm), a search algorithm based on the mechanics of natural selection and natural genetics, is used to optimize the λi The new method is named as GAPPSV algorithm. The simulation results show that GAPPSV algorithm is very efficient because the control process is short and the steady-state error is small.

关键词： stabilization chaotic time series genetic algorithm

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Multi-step-prediction of chaotic time series based on co-evolutionary recurrent neural network

引用

Chinese Physics B 2008年第2期17卷 536-542页

作者：马千里郑启伦彭宏钟谭卫覃姜维 College of Computer Science and Engineering South China University of Technology College of Science South China Agriculture University

This paper proposes a co-evolutionary recurrent neural network （CERNN） for the multi-step-prediction of chaotic time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent neural networks by coevolutionary strategy. The searching space was separated into two subspaces and the individuals are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability of recurrent neural network to incorporate past experience due to internal recurrence. The effectiveness of CERNN is evaluated by using three benchmark chaotic time series data sets： the Lorenz series, Mackey-Glass series and real-world sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic time series.

关键词： chaotic time series multi-step-prediction co-evolutionary strategy recurrent neural networks

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

New prediction of chaotic time series based on local Lyapunov exponent

引用

Chinese Physics B 2013年第5期22卷 191-197页

作者：张勇 Department of Mathematics and Computer Wuhan Polytechnic University

A new method of predicting chaotic time series is presented based on a local Lyapunov exponent, by quantitatively measuring the exponential rate of separation or attraction of two infinitely close trajectories in state space. After recon- structing state space from one-dimensional chaotic time series, neighboring multiple-state vectors of the predicting point are selected to deduce the prediction formula by using the definition of the locaI Lyapunov exponent. Numerical simulations are carded out to test its effectiveness and verify its higher precision over two older methods. The effects of the number of referential state vectors and added noise on forecasting accuracy are also studied numerically.

关键词： chaotic time series prediction of chaotic time series local Lyapunov exponent least squaresmethod

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

chaotic time series prediction using least squares support vector machines

引用

Chinese Physics B 2004年第4期13卷 454-458页

作者：叶美盈汪晓东 CollegeofMathematicsandPhysics ZhejiangNormalUniversityJinhua321004China CollegeofInformationScienceandEngineering ZhejiangNormalUniversityJinhua321004China

We propose a new technique of using the least squares support vector machines (LS-SVMs) for making one-step and multi-step prediction of chaotic time series. The LS-SVM achieves higher generalization performance than traditional neural networks and provides an accurate chaotic time series prediction. Unlike neural networks' training that requires nonlinear optimization with the danger of getting stuck into local minima, training LS-SVM is equivalent to solving a set of linear equations. Thus it has fast convergence. The simulation results show that LS-SVM has much better potential in the field of chaotic time series prediction.

关键词： chaotic time series time series prediction support vector machines

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

chaotic time series prediction using mean-field theory for support vector machine

引用

Chinese Physics B 2005年第5期14卷 922-929页

作者：崔万照朱长纯保文星刘君华 SchoolofElectronicsandInformationEngineering Xi'anJiaotongUniversityXi'an710049China SchoolofElectronicsandInformationEngineering Xi'anJiaotongUniversityXi'an710049China

This paper presents a novel method for predicting chaotic time series which is based on the support vector machines approach, and it uses the mean-field theory for developing an easy and efficient learning procedure for the support vector machine. The proposed method approximates the distribution of the support vector machine parameters to a Gaussian process and uses the mean-field theory to estimate these parameters easily, and select the weights of the mixture of kernels used in the support vector machine estimation more accurately and faster than traditional quadratic programming-based algorithms. Finally, relationships between the embedding dimension and the predicting performance of this method are discussed, and the Mackey-Glass equation is applied to test this method. The stimulations show that the mean-field theory for support vector machine can predict chaotic time series accurately, and even if the embedding dimension is unknown, the predicted results are still satisfactory. This result implies that the mean-field theory for support vector machine is a good tool for studying chaotic time series.

关键词： chaotic time series support vector machine mean-field theory

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

AN ANALYTIC AND APPLICATION TO STATE SPACE RECONSTRUCTION ABOUT chaotic time series

引用

Applied Mathematics and Mechanics(English Edition) 2000年第11期21卷 1237-1245页

作者：马军海陈予恕 School of Management Tianjin UniversityTianjin 300072P R China Department of Mechanics Tianjin UniversityTianjin 300072P R China

The state space, reconstruction is the major important quantitative index for describing non-linear chaotic time series. Based on the work of many scholars, such as: AT. H. Packard, F. Takens, M. Casdagli, J. F. Gibson, CHEN Yu-shu et al, the state space was reconstructed using the method of Legendre coordinate. Several different scaling regimes for lag time tau were identified. The influence for state space reconstruction of lag time tau was discussed. The result tells us that is a good practical method for state space reconstruction.

关键词： chaotic time series state space reconstruction Legendre coordinates?

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

PREDICTION TECHNIQUES OF chaotic time series AND ITS APPLICATIONS AT LOW NOISE LEVEL

引用

Applied Mathematics and Mechanics(English Edition) 2006年第1期27卷 7-14页

作者：马军海王志强陈予恕 School of Management Department of Mechanics

The paper not only studies the noise reduction methods of chaotic time series with noise and its reconstruction techniques, but also discusses prediction techniques of chaotic time series and its applications based on chaotic data noise reduction. In the paper, we first decompose the phase space of chaotic time series to range space and null noise space. Secondly we restructure original chaotic time series in range space. Lastly on the basis of the above, we establish order of the nonlinear model and make use of the nonlinear model to predict some research. The result indicates that the nonlinear model has very strong ability of approximation function, and Chaos predict method has certain tutorial significance to the practical problems.

关键词： chaotic time series noise reduction essential characteristic extraction nonlinear model predict technology

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：