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A Cross-Reference Method for Nonlinear Time Series Analysis in Semi-Blind Case
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作者 杨绿溪 何振亚 《Journal of Southeast University(English Edition)》 EI CAS 1999年第1期3-8,共6页
In this paper, we propose a cross reference method for nonlinear time series analyzing in semi blind case, that is, the dynamic equations modeling the time series are known but the corresponding parameters are not. ... In this paper, we propose a cross reference method for nonlinear time series analyzing in semi blind case, that is, the dynamic equations modeling the time series are known but the corresponding parameters are not. The tasks of noise reduction and parameter estimation which were fulfilled separately before are combined iteratively. With the positive interaction between the two processing modules, the method is somewhat superior. Some prior work can be viewed as special cases of this general framework. The simulations for noise reduction and parameter estimation of contaminated chaotic time series show improved performance of our method compared with previous work. 展开更多
关键词 nonlinear time series analysis noise reduction parameter estimation cross reference
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Discussion About Nonlinear Time Series Prediction Using Least Squares Support Vector Machine 被引量:3
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作者 XURui-Rui BIANGuo-Xin GAOChen-Feng CHENTian-Lun 《Communications in Theoretical Physics》 SCIE CAS CSCD 2005年第6期1056-1060,共5页
The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we e... The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values.. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved. 展开更多
关键词 least squares support vector machine nonlinear time series PREDICTION CLUSTERING
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Nonlinear Time Series Prediction Using Chaotic Neural Networks 被引量:3
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作者 LIKe-Ping CHENTian-Lun 《Communications in Theoretical Physics》 SCIE CAS CSCD 2001年第6期759-762,共4页
A nonlinear feedback term is introduced into the evaluation equation of weights of the backpropagation algorithm for neural network, the network becomes a chaotic one. For the purpose of that we can investigate how th... A nonlinear feedback term is introduced into the evaluation equation of weights of the backpropagation algorithm for neural network, the network becomes a chaotic one. For the purpose of that we can investigate how the different feedback terms affect the process of learning and forecasting, we use the model to forecast the nonlinear time series which is produced by Makey-Glass equation. By selecting the suitable feedback term, the system can escape from the local minima and converge to the global minimum or its approximate solutions, and the forecasting results are better than those of backpropagation algorithm. 展开更多
关键词 neural network chaotic dynamics forecasting nonlinear time series
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AUTO-EXTRACTING TECHNIQUE OF DYNAMIC CHAOS FEATURES FOR NONLINEAR TIME SERIES 被引量:6
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作者 CHEN Guo 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2006年第4期524-529,共6页
The main purpose of nonlinear time series analysis is based on the rebuilding theory of phase space, and to study how to transform the response signal to rebuilt phase space in order to extract dynamic feature informa... The main purpose of nonlinear time series analysis is based on the rebuilding theory of phase space, and to study how to transform the response signal to rebuilt phase space in order to extract dynamic feature information, and to provide effective approach for nonlinear signal analysis and fault diagnosis of nonlinear dynamic system. Now, it has already formed an important offset of nonlinear science. But, traditional method cannot extract chaos features automatically, and it needs man's participation in the whole process. A new method is put forward, which can implement auto-extracting of chaos features for nonlinear time series. Firstly, to confirm time delay r by autocorrelation method; Secondly, to compute embedded dimension m and correlation dimension D; Thirdly, to compute the maximum Lyapunov index λmax; Finally, to calculate the chaos degree Dch of Poincare map, and the non-circle degree Dnc and non-order degree Dno of quasi-phase orbit. Chaos features extracting has important meaning to fault diagnosis of nonlinear system based on nonlinear chaos features. Examples show validity of the proposed method. 展开更多
关键词 nonlinear time series analysis Chaos Feature extracting Fault diagnosis
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Denoising Nonlinear Time Series Using Singular Spectrum Analysis and Fuzzy Entropy 被引量:1
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作者 江剑 谢洪波 《Chinese Physics Letters》 SCIE CAS CSCD 2016年第10期19-23,共5页
We present a hybrid singular spectrum analysis (SSA) and fuzzy entropy method to filter noisy nonlinear time series. With this approach, SSA decomposes the noisy time series into its constituent components including... We present a hybrid singular spectrum analysis (SSA) and fuzzy entropy method to filter noisy nonlinear time series. With this approach, SSA decomposes the noisy time series into its constituent components including both the deterministic behavior and noise, while fuzzy entropy automatically differentiates the optimal dominant components from the noise based on the complexity of each component. We demonstrate the effectiveness of the hybrid approach in reconstructing the Lorenz and Mackey--Class attractors, as well as improving the multi-step prediction quality of these two series in noisy environments. 展开更多
关键词 of on or in Denoising nonlinear time series Using Singular Spectrum Analysis and Fuzzy Entropy NLP IS
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Correlation between detrended fluctuation analysis and the Lempel-Ziv complexity in nonlinear time series analysis 被引量:1
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作者 唐友福 刘树林 +1 位作者 姜锐红 刘颖慧 《Chinese Physics B》 SCIE EI CAS CSCD 2013年第3期219-225,共7页
We study the correlation between detrended fluctuation analysis(DFA) and the Lempel-Ziv complexity(LZC) in nonlinear time series analysis in this paper.Typical dynamic systems including a logistic map and a Duffin... We study the correlation between detrended fluctuation analysis(DFA) and the Lempel-Ziv complexity(LZC) in nonlinear time series analysis in this paper.Typical dynamic systems including a logistic map and a Duffing model are investigated.Moreover,the influence of Gaussian random noise on both the DFA and LZC are analyzed.The results show a high correlation between the DFA and LZC,which can quantify the non-stationarity and the nonlinearity of the time series,respectively.With the enhancement of the random component,the exponent α and the normalized complexity index C show increasing trends.In addition,C is found to be more sensitive to the fluctuation in the nonlinear time series than α.Finally,the correlation between the DFA and LZC is applied to the extraction of vibration signals for a reciprocating compressor gas valve,and an effective fault diagnosis result is obtained. 展开更多
关键词 nonlinear time series detrended fluctuation analysis Lempel-Ziv complexity correlation coefficient
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Nonlinear Time Series Prediction Using LS-SVM with Chaotic Mutation Evolutionary Programming for Parameter Optimization 被引量:1
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作者 XU Rui-Rui CHEN Tian-Lun GAO Cheng-Feng 《Communications in Theoretical Physics》 SCIE CAS CSCD 2006年第4期641-646,共6页
Nonlinear time series prediction is studied by using an improved least squares support vector machine (LSSVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimizatio... Nonlinear time series prediction is studied by using an improved least squares support vector machine (LSSVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimization. We analyze how the prediction error varies with different parameters (σ, γ) in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained. 展开更多
关键词 nonlinear time series prediction least squares support vector machine chaotic mutation evolu tionary programming
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Discussion of Some Problems About Nonlinear Time Series Prediction Using v-Support Vector Machine
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作者 GAO Cheng-Feng CHEN Tian-Lun NAN Tian-Shi Department of Physics,Nankai University,Tianjin 300071,China 《Communications in Theoretical Physics》 SCIE CAS CSCD 2007年第7期117-124,共8页
Some problems in using v-support vector machine(v-SVM)for the prediction of nonlinear time series arediscussed.The problems include selection of various net parameters,which affect the performance of prediction,mixtur... Some problems in using v-support vector machine(v-SVM)for the prediction of nonlinear time series arediscussed.The problems include selection of various net parameters,which affect the performance of prediction,mixtureof kernels,and decomposition cooperation linear programming v-SVM regression,which result in improvements of thealgorithm.Computer simulations in the prediction of nonlinear time series produced by Mackey-Glass equation andLorenz equation provide some improved results. 展开更多
关键词 v-SVM nonlinear time series prediction
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A Prediction Method Based on Improved Echo State Network for COVID-19 Nonlinear Time Series
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作者 Banteng Liu Wei Chen +3 位作者 Yourong Chen Ping Sun Heli Jin Hao Chen 《Journal of Computer and Communications》 2020年第12期113-122,共10页
<div style="text-align:justify;"> This paper proposes a prediction method based on improved Echo State Network for COVID-19 nonlinear time series, which improves the Echo State Network from the reservo... <div style="text-align:justify;"> This paper proposes a prediction method based on improved Echo State Network for COVID-19 nonlinear time series, which improves the Echo State Network from the reservoir topology and the output weight matrix, and adopt the ABC (Artificial Bee Colony) algorithm based on crossover and crowding strategy to optimize the parameters. Finally, the proposed method is simulated and the results show that it has stronger prediction ability for COVID-19 nonlinear time series. </div> 展开更多
关键词 COVID-19 nonlinear time series PREDICTION Echo State Network
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LEARNING GRANGER CAUSALITY GRAPHS FOR MULTIVARIATE NONLINEAR TIME SERIES 被引量:3
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作者 Wei GAO Zheng TIAN 《Journal of Systems Science and Systems Engineering》 SCIE EI CSCD 2009年第1期38-52,共15页
An information theory method is proposed to test the. Granger causality and contemporaneous conditional independence in Granger causality graph models. In the graphs, the vertex set denotes the component series of the... An information theory method is proposed to test the. Granger causality and contemporaneous conditional independence in Granger causality graph models. In the graphs, the vertex set denotes the component series of the multivariate time series, and the directed edges denote causal dependence, while the undirected edges reflect the instantaneous dependence. The presence of the edges is measured by a statistics based on conditional mutual information and tested by a permutation procedure. Furthermore, for the existed relations, a statistics based on the difference between general conditional mutual information and linear conditional mutual information is proposed to test the nonlinearity. The significance of the nonlinear test statistics is determined by a bootstrap method based on surrogate data. We investigate the finite sample behavior of the procedure through simulation time series with different dependence structures, including linear and nonlinear relations. 展开更多
关键词 Multivariate nonlinear time series Granger causality graph conditional mutual information surrogate data
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Nonlinear Analysis of Physiological Time Series 被引量:1
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作者 MENG Qing-fang PENG Yu-hua +1 位作者 XUE Yu-li HAN Min 《Chinese Journal of Biomedical Engineering(English Edition)》 2007年第4期163-169,共7页
The heart rate variability could be explained by a low-dimensional governing mechanism. There has been increasing interest in verifying and understanding the coupling between the respiration and the heart rate. In thi... The heart rate variability could be explained by a low-dimensional governing mechanism. There has been increasing interest in verifying and understanding the coupling between the respiration and the heart rate. In this paper we use the nonlinear detection method to detect the nonlinear deterministic component in the physiological time series by a single variable series and two variables series respectively, and use the conditional information entropy to analyze the correlation between the heart rate, the respiration and the blood oxygen concentration. The conclusions are that there is the nonlinear deterministic component in the heart rate data and respiration data, and the heart rate and the respiration are two variables originating from the same underlying dynamics. 展开更多
关键词 nonlinear time series analysis nonlinear detection conditional information entropy heart rate variability
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Ergodicity of a Class of Nonlinear Time Series Models in Random Environment Domain
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作者 En-wen Zhu Han-jun Zhang +3 位作者 Gang Yang Zai-ming Liu Jie-zhong Zou Shao-shun Long 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2010年第1期159-168,共10页
In this paper, we study the problem of a variety of p, onlinear time series model Xn+ 1= TZn+1(X(n), … ,X(n - Zn+l), en+1(Zn+1)) in which {Zn} is a Markov chain with finite state space, and for every sta... In this paper, we study the problem of a variety of p, onlinear time series model Xn+ 1= TZn+1(X(n), … ,X(n - Zn+l), en+1(Zn+1)) in which {Zn} is a Markov chain with finite state space, and for every state i of the Markov chain, {en(i)} is a sequence of independent and identically distributed random variables. Also, the limit behavior of the sequence {Xn} defined by the above model is investigated. Some new novel results on the underlying models are presented. 展开更多
关键词 ERGODICITY Random environment nonlinear time series
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Nonlinear Time Series Model for Shape Classification Using Neural Networks
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作者 熊沈蜀 周兆英 《Tsinghua Science and Technology》 SCIE EI CAS 2000年第4期374-377,共4页
关键词 nonlinear time series Model for Shape Classification Using Neural Networks
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Small-time scale network traffic prediction based on a local support vector machine regression model 被引量:10
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作者 孟庆芳 陈月辉 彭玉华 《Chinese Physics B》 SCIE EI CAS CSCD 2009年第6期2194-2199,共6页
In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the... In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements. 展开更多
关键词 network traffic small-time scale nonlinear time series analysis support vector machine regression model
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Nonlinear combined forecasting model based on fuzzy adaptive variable weight and its application 被引量:1
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作者 蒋爱华 梅炽 +1 位作者 鄂加强 时章明 《Journal of Central South University》 SCIE EI CAS 2010年第4期863-867,共5页
In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using concept... In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system. 展开更多
关键词 nonlinear combined forecasting nonlinear time series method of fuzzy adaptive variable weight relative error adaptive control coefficient
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A new method of determining the optimal embedding dimension based on nonlinear prediction 被引量:1
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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
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Monthly and seasonal streamflow forecasting of large dryland catchments in Brazil
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作者 Alexandre C COSTA Alvson B S ESTACIO +1 位作者 Francisco de A de SOUZA FILHO Iran E LIMA NETO 《Journal of Arid Land》 SCIE CSCD 2021年第3期205-223,共19页
Streamflow forecasting in drylands is challenging.Data are scarce,catchments are highly humanmodified and streamflow exhibits strong nonlinear responses to rainfall.The goal of this study was to evaluate the monthly a... Streamflow forecasting in drylands is challenging.Data are scarce,catchments are highly humanmodified and streamflow exhibits strong nonlinear responses to rainfall.The goal of this study was to evaluate the monthly and seasonal streamflow forecasting in two large catchments in the Jaguaribe River Basin in the Brazilian semi-arid area.We adopted four different lead times:one month ahead for monthly scale and two,three and four months ahead for seasonal scale.The gaps of the historic streamflow series were filled up by using rainfall-runoff modelling.Then,time series model techniques were applied,i.e.,the locally constant,the locally averaged,the k-nearest-neighbours algorithm(k-NN)and the autoregressive(AR)model.The criterion of reliability of the validation results is that the forecast is more skillful than streamflow climatology.Our approach outperformed the streamflow climatology for all monthly streamflows.On average,the former was 25%better than the latter.The seasonal streamflow forecasting(SSF)was also reliable(on average,20%better than the climatology),failing slightly only for the high flow season of one catchment(6%worse than the climatology).Considering an uncertainty envelope(probabilistic forecasting),which was considerably narrower than the data standard deviation,the streamflow forecasting performance increased by about 50%at both scales.The forecast errors were mainly driven by the streamflow intra-seasonality at monthly scale,while they were by the forecast lead time at seasonal scale.The best-fit and worst-fit time series model were the k-NN approach and the AR model,respectively.The rainfall-runoff modelling outputs played an important role in improving streamflow forecasting for one streamgauge that showed 35%of data gaps.The developed data-driven approach is mathematical and computationally very simple,demands few resources to accomplish its operational implementation and is applicable to other dryland watersheds.Our findings may be part of drought forecasting systems and potentially help allocating water months in advance.Moreover,the developed strategy can serve as a baseline for more complex streamflow forecast systems. 展开更多
关键词 nonlinear time series analysis probabilistic streamflow forecasting reconstructed streamflow data dryland hydrology rainfall-runoff modelling stochastic dynamical systems
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Cross and joint ordinal partition transition networks for multivariate time series analysis
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作者 Heng Guo Jia-Yang Zhang +1 位作者 Yong Zou Shu-Guang Guan 《Frontiers of physics》 SCIE CSCD 2018年第5期203-212,共10页
We propose the construction of cross and joint ordinal pattern transition networks from multivariate time series for two coupled systems, where synchronizations are often present. In particular, we focus on phase sync... We propose the construction of cross and joint ordinal pattern transition networks from multivariate time series for two coupled systems, where synchronizations are often present. In particular, we focus on phase synchronization, which is a prototypical scenario in dynamical systems. We systematically show that cross and joint ordinal pattern transition networks are sensitive to phase synchronization. Furthermore, we find that some particular missing ordinal patterns play crucial roles in forming the detailed structures in the parameter space, whereas the calculations of permutation entropy measures often do not. We conclude that cross and joint ordinal partition transition network approaches provide complementary insights into the traditional symbolic analysis of synchronization transitions. 展开更多
关键词 nonlinear time series analysis complex networks ordinal pattern partition transitionnetwork phase synchronization
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Detecting Lags in Nonlinear Models Using General Mutual Information 被引量:1
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作者 Wei GAO1,2, Zheng TIAN1,3 1. Department of Applied Mathematics, Northwest Polytechnical University, Shaanxi 710072, P. R. China 2. School of Statistics, Xi’an University of Finance & Economics, Shaanxi 710061, P. R. China 3. National Key Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, P. R. China 《Journal of Mathematical Research and Exposition》 CSCD 2010年第1期87-98,共12页
The general mutual information (GMI) and general conditional mutual information (GCMI) are considered to measure lag dependences in nonlinear time series. Both of the measures have the property of invariance with ... The general mutual information (GMI) and general conditional mutual information (GCMI) are considered to measure lag dependences in nonlinear time series. Both of the measures have the property of invariance with transform. The statistics based on GMI and GCMI are estimated using the correlation integral. Under the hypothesis of independent series, the estimators have Gaussian asymptotic distributions. Simulations applied to generated nonlinear series demonstrate that the methods appear to find frequently the correct lags. 展开更多
关键词 general mutual information general conditional mutual information nonlinear time series lag dependence.
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THE EXISTENCE OF MOMENTS OF NONLINEAR AUTOREGRESSIVE MODEL 被引量:1
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作者 陈敏 安鸿志 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 1998年第3期328-332,共5页
In this paper we examine the existence of higher-order moments for the following nonlinearautoregressive model
关键词 nonlinear time series Markov chain the higher-order moments
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