期刊文献+
共找到9篇文章
< 1 >
每页显示 20 50 100
Optimization of support vector machine power load forecasting model based on data mining and Lyapunov exponents 被引量:7
1
作者 牛东晓 王永利 马小勇 《Journal of Central South University》 SCIE EI CAS 2010年第2期406-412,共7页
According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are comput... According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting. 展开更多
关键词 power load forecasting support vector machine (SVM) Lyapunov exponent data mining embedding dimension feature classification
下载PDF
PARAMETERS DETERMINATION METHOD OF PHASE-SPACE RECONSTRUCTION BASED ON DIFFERENTIAL ENTROPY RATIO AND RBF NEURAL NETWORK 被引量:4
2
作者 Zhang Shuqing Hu Yongtao +1 位作者 Bao Hongyan Li Xinxin 《Journal of Electronics(China)》 2014年第1期61-67,共7页
Phase space reconstruction is the first step of recognizing the chaotic time series.On the basis of differential entropy ratio method,the embedding dimension opt m and time delay t are optimal for the state space reco... Phase space reconstruction is the first step of recognizing the chaotic time series.On the basis of differential entropy ratio method,the embedding dimension opt m and time delay t are optimal for the state space reconstruction could be determined.But they are not the optimal parameters accepted for prediction.This study proposes an improved method based on the differential entropy ratio and Radial Basis Function(RBF)neural network to estimate the embedding dimension m and the time delay t,which have both optimal characteristics of the state space reconstruction and the prediction.Simulating experiments of Lorenz system and Doffing system show that the original phase space could be reconstructed from the time series effectively,and both the prediction accuracy and prediction length are improved greatly. 展开更多
关键词 Phase-space reconstruction Chaotic time series Differential entropy ratio Embedding dimension Time delay Radial Basis Function(RBF) neural network
下载PDF
Research on the chaos recognition method based on differential entropy 被引量:2
3
作者 张淑清 赵玉春 +2 位作者 贾健 张立国 上官寒露 《Chinese Physics B》 SCIE EI CAS CSCD 2010年第6期169-176,共8页
Phase space reconstruction is the first step to recognizing the chaos from observed time series. On the basis of differential entropy, this paper introduces an efficient method to estimate the embedding dimension and ... Phase space reconstruction is the first step to recognizing the chaos from observed time series. On the basis of differential entropy, this paper introduces an efficient method to estimate the embedding dimension and the time delay simultaneously. The differential entropy is used to characterize the disorder degree of the reconstructed attractor. The minimum value of the differential entropy corresponds to the optimum set of the reconstructed parameters. Simulated experiments show that the original phase space can be effectively reconstructed from time series, and the accuracy of the invariants in phase space reconstruction is greatly improved. It provides a new method for the identification of chaotic signals from time series. 展开更多
关键词 CHAOS differential entropy embedding dimension time delay
下载PDF
A new method of determining the optimal embedding dimension based on nonlinear prediction 被引量:1
4
作者 孟庆芳 彭玉华 薛佩军 《Chinese Physics B》 SCIE EI CAS CSCD 2007年第5期1252-1257,共6页
A new method is proposed to determine the optimal embedding dimension from a scalar time series in this paper. This method determines the optimal embedding dimension by optimizing the nonlinear autoregressive predicti... A new method is proposed to determine the optimal embedding dimension from a scalar time series in this paper. This method determines the optimal embedding dimension by optimizing the nonlinear autoregressive prediction model parameterized by the embedding dimension and the nonlinear degree. Simulation results show the effectiveness of this method. And this method is applicable to a short time series, stable to noise, computationally efficient, and without any purposely introduced parameters. 展开更多
关键词 embedding dimension nonlinear autoregressive prediction model nonlinear time series
下载PDF
Parameter optimization of complex network based on the change-point identification
5
作者 Xu Xingtao Tao Jiagui 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2023年第6期22-29,共8页
This paper proposes a novel method for the parameter optimization of complex networks established through coarsening and phase space reconstruction.Firstly,we identify the change-points of the time series based on the... This paper proposes a novel method for the parameter optimization of complex networks established through coarsening and phase space reconstruction.Firstly,we identify the change-points of the time series based on the cumulative sum(CUSUM)control chart method.Then,we optimize the coarse-graining parameters and phase space embedding dimension based on the evolution analysis of the global topology index(betweenness)at the mutation point.Finally,we conduct a simulation analysis based on real-time data of Chinese copper spot prices.The results show that the delay of the copper spot prices in Chinese spot market is 1 day,and the optimal embedding dimension of the phase space reconstruction is 3.The acceptance level of the investors towards the small fluctuations in copper spot prices is 0.2 times than the average level of price fluctuations,which means that an average price fluctuation of 0.2 times is the optimal coarse-graining parameter. 展开更多
关键词 complex network CHANGE-POINT COARSE-GRAINING embedding dimension
原文传递
State Prediction of Chaotic System Based on ANN Model 被引量:1
6
作者 YUE Yi-hong, HAN Wen-xiuManagement School, Tianjin University, Tianjin 300072, China 《Systems Science and Systems Engineering》 CSCD 2002年第3期306-312,共7页
The choice of time delay and embedding dimension is very important to the phase space reconstruction of any chaotic time series. In this paper, we determine optimal time delay by computing autocorrelation function of ... The choice of time delay and embedding dimension is very important to the phase space reconstruction of any chaotic time series. In this paper, we determine optimal time delay by computing autocorrelation function of time series. Optimal embedding dimension is given by means of the relation between embedding dimension and correlation dimension of chaotic time series. Based on the methods above, we choose ANN model to appoximate the given true system. At the same time, a new algorithm is applied to determine the network weights. At the end of this paper, the theory above is demonstrated through the research of time series generated by Logistic map. 展开更多
关键词 CHAOS autocorrelation function optimal embedding dimension optimal time delay Logistic map
原文传递
Selection of Embedding Dimension and Delay Time in Phase Space Reconstruction 被引量:1
7
作者 MA Hong-guang HAN Chong-zhao 《Frontiers of Electrical and Electronic Engineering in China》 CSCD 2006年第1期111-114,共4页
A new algorithm is proposed for computing the embedding dimension and delay time in phase space reconstruction.It makes use of the zero of the nonbias multiple autocorrelation function of the chaotic time series to de... A new algorithm is proposed for computing the embedding dimension and delay time in phase space reconstruction.It makes use of the zero of the nonbias multiple autocorrelation function of the chaotic time series to determine the time delay,which efficiently depresses the computing error caused by tracing arbitrarily the slop variation of average displacement(AD)in AD algorithm.Thereafter,by means of the iterative algorithm of multiple autocorrelation andΓtest,the near-optimum parameters of embedding dimension and delay time are estimated.This algorithm is provided with a sound theoretic basis,and its computing complexity is relatively lower and not strongly dependent on the data length.The simulated experimental results indicate that the relative error of the correlation dimension of standard chaotic time series is decreased from 4.4%when using conventional algorithm to 1.06%when using this algorithm.The accuracy of invariants in phase space reconstruction is greatly improved. 展开更多
关键词 phase space reconstruction embedding dimension delay time multiple autocorrelation Γtest
原文传递
On Some Numerical Semigroup Transforms
8
作者 Carmelo Cisto 《Algebra Colloquium》 SCIE CSCD 2022年第3期509-526,共18页
In this paper we introduce a particular semigroup transform A that fixes the invariants involved in Wilf's conjecture,except the embedding dimension.It also allows one to arrange the set of non-ordinary and non-ir... In this paper we introduce a particular semigroup transform A that fixes the invariants involved in Wilf's conjecture,except the embedding dimension.It also allows one to arrange the set of non-ordinary and non-irreducible numerical semigroups in a family of rooted trees.In addition,we study another transform,having similar features,that has been introduced by Bras-Amorós,and we make a comparison of them.In particular,we study the behavior of the embedding dimension under the action of such transforms,providing some consequences concerning Wilf's conjecture. 展开更多
关键词 numerical semigroup embedding dimension GENUS left element Wilf's conjecture
原文传递
Nonlinear Time Series Analysis Since 1990:Some Personal Reflections 被引量:4
9
作者 Howel Tong 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2002年第2期177-184,共8页
I reflect upon the development of nonlinear time series analysis since 1990 by focusing on five major areas of development. These areas include the interface between nonlinear time series analysis and chaos, the nonpa... I reflect upon the development of nonlinear time series analysis since 1990 by focusing on five major areas of development. These areas include the interface between nonlinear time series analysis and chaos, the nonparametric/semiparametric approach, nonlinear state space modelling, financial time series and nonlinear modelling of panels of time series. 展开更多
关键词 CHAOS common structure curse of dimensionality embedding dimension financial time series initial value sensitivity local polynomial smoother long memory Markov chain Monte Carlo nonlinear dynamical systems nonlinear state space models
全文增补中
上一页 1 下一页 到第
使用帮助 返回顶部