Phase space is one of the most important parameters used to describe beam properties. Computer tomography, as a method for reconstructing phase space and measuring beam emittance, has been used in many accelerators ov...Phase space is one of the most important parameters used to describe beam properties. Computer tomography, as a method for reconstructing phase space and measuring beam emittance, has been used in many accelerators over the past few decades. In this paper, we demonstrate a transverse phase space reconstruction study in the Shanghai soft X-ray free electron laser facility. First,we discuss the basic principles of phase space reconstruction and the advantage of reconstructing beam distribution in normalized phase space. Then, the phase space reconstruction results by different computer tomography methods based on the maximum entropy(MENT) algorithm and the filtered back projection algorithm in normalized phase space are presented. The simulation results indicate that,with proper configuration of the phase advance between adjacent screens, the MENT algorithm is feasible and has good efficiency. The beam emittance and Twiss parameters are also calculated using the reconstructed phase space.展开更多
To make elevator group control system better follow the change of elevator traffic flow (ETF) in order to adjust the control strategy,the prediction method of support vector machine (SVM) in combination with phase spa...To make elevator group control system better follow the change of elevator traffic flow (ETF) in order to adjust the control strategy,the prediction method of support vector machine (SVM) in combination with phase space reconstruction has been proposed for ETF.Firstly,the phase space reconstruction for elevator traffic flow time series (ETFTS) is processed.Secondly,the small data set method is applied to calculate the largest Lyapunov exponent to judge the chaotic property of ETF.Then prediction model of ETFTS based on SVM is founded.Finally,the method is applied to predict the time series for the incoming and outgoing passenger flow respectively using ETF data collected in some building.Meanwhile,it is compared with RBF neural network model.Simulation results show that the trend of factual traffic flow is better followed by predictive traffic flow.SVM algorithm has much better prediction performance.The fitting and prediction of ETF with better effect are realized.展开更多
The degradation process of organosol coated tinplate in beverage was investigated by electrochemical noise (EN) technique combined with morphology characterization.EN data were analyzed using phase space reconstructio...The degradation process of organosol coated tinplate in beverage was investigated by electrochemical noise (EN) technique combined with morphology characterization.EN data were analyzed using phase space reconstruction theory.With the correlation dimensions obtained from the phase space reconstruction,the chaotic behavior of EN was quantitatively evaluated.The results show that both electrochemical potential noise (EPN) and electrochemical current noise (ECN) have chaotic properties.The correlation dimensions of EPN increase with corrosion extent,while those of ECN seem nearly unchanged.The increased correlation dimensions of EPN during the degradation process are associated with the increased susceptibility to local corrosion.展开更多
In view of the disadvantages of the traditional phase space reconstruction method, this paper presents the method of phase space reconstruction based on the wavelet decomposition and indicates that the wavelet decompo...In view of the disadvantages of the traditional phase space reconstruction method, this paper presents the method of phase space reconstruction based on the wavelet decomposition and indicates that the wavelet decomposition of chaotic dynamical system is essentially a projection of chaotic attractor on the axes of space opened by the wavelet filter vectors, which corresponds to the time-delayed embedding method of phase space reconstruction proposed by Packard and Takens. The experimental results show that, the structure of dynamical trajectory of chaotic system on the wavelet space is much similar to the original system, and the nonlinear invariants such as correlation dimension, Lyapunov exponent and Kolmogorov entropy are still reserved. It demonstrates that wavelet decomposition is effective for characterizing chaotic dynamical system.展开更多
Proposed a new method to disclose the complicated non-linearity structure of the water-resource system, introducing chaos theory into the hydrology and water resources field, and combined with the chaos theory and art...Proposed a new method to disclose the complicated non-linearity structure of the water-resource system, introducing chaos theory into the hydrology and water resources field, and combined with the chaos theory and artificial neural networks. Training data construction and networks structure were determined by the phase space reconstruction, and establishing nonlinear relationship of phase points with neural networks, the forecasting model of the resource quantity of the surface water was brought forward. The keystone of the way and the detailed arithmetic of the network training were given. The example shows that the model has highly forecasting precision.展开更多
Power Quality (PQ) combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The cl...Power Quality (PQ) combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The classification system consists of two parts, namely the feature extraction and the automatic recognition. In the feature extraction stage, Phase Space Reconstruction (PSR), a time series analysis tool, is utilized to construct disturbance signal trajectories. For these trajectories, several indices are proposed to form the feature vectors. Support Vector Machines (SVMs) are then implemented to recognize the different patterns and to evaluate the efficiencies. The types of disturbances discussed include a combination of short-term dis-turbances (voltage sags, swells) and long-term disturbances (flickers, harmonics), as well as their homologous single ones. The feasibilities of the proposed approach are verified by simulation with thousands of PQ events. Comparison studies based on Wavelet Transform (WT) and Artificial Neural Network (ANN) are also reported to show its advantages.展开更多
A new method of nonlinear analysis is established by combining phase space reconstruction and data reduction sub-frequency band wavelet. This method is applied to two types of chaotic dynamic systems(Lorenz and Rssler...A new method of nonlinear analysis is established by combining phase space reconstruction and data reduction sub-frequency band wavelet. This method is applied to two types of chaotic dynamic systems(Lorenz and Rssler) to examine the anti-noise ability for complex systems. Results show that the nonlinear dynamic system analysis method resists noise and reveals the internal dynamics of a weak signal from noise pollution. On this basis, the vertical upward gas–liquid two-phase flow in a 2 mm × 0.81 mm small rectangular channel is investigated. The frequency and energy distributions of the main oscillation mode are revealed by analyzing the time–frequency spectra of the pressure signals of different flow patterns. The positive power spectral density of singular-value frequency entropy and the damping ratio are extracted to characterize the evolution of flow patterns and achieve accurate recognition of different vertical upward gas–liquid flow patterns(bubbly flow:100%, slug flow: 92%, churn flow: 96%, annular flow: 100%). The proposed analysis method will enrich the dynamics theory of multi-phase flow in small channel.展开更多
Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body...Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress conditions.Current monitoring data processing methods cannot fully consider the complexity of monitoring objects,the diversity of monitoring methods,and the dynamics of monitoring data.To solve this problem,this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock combinations.The three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress,multipoint displacement meter,and inclinometer.Varied information,such as the stress and displacement of the filling body–surrounding rock combination,was continuously obtained.Combined with the average mutual information method and the false nearest neighbor point method,the phase space of the heterogeneous information of the filling body–surrounding rock combination was then constructed.In this paper,the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock combination.The evaluated distances(ED)revealed a high sensitivity to the stability of the filling body–surrounding rock combination.The new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou Mine.The moments of mutation in these time series were at least 3 months ahead of the roadway return dates.In the ED prediction experiments,the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models(long short-term memory and Transformer).Furthermore,the root-mean-square error distribution of the prediction results peaked at 0.26,thus outperforming the no-prediction method in 70%of the cases.展开更多
In order to manage and control semiconductor wafer fabrication system (SWFS) more effectively,the daily throughput prediction data of wafer fab are often used in the planning and scheduling of SWFS.In this paper,an ar...In order to manage and control semiconductor wafer fabrication system (SWFS) more effectively,the daily throughput prediction data of wafer fab are often used in the planning and scheduling of SWFS.In this paper,an artificial neural network (ANN) prediction method based on phase space reconstruction (PSR) and ant colony optimization (ACO) is presented,in which the phase space reconstruction theory is used to reconstruct the daily throughput time series,the ANN is used to construct the daily throughput prediction model,and the ACO is used to train the connection weight and bias values of the neural network prediction model.Testing with factory operation data and comparing with the traditional method show that the proposed methodology is effective.展开更多
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
In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine ...In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine proper minimum embedding dimension is constructed. This method has a sound theoretical basis and can lead to good result. It can indicate the noise level in the data to be reconstructed, and estimate the reconstruction quality. It is applied to speech signal reconstruction and the generic embedding dimension of speech signals is deduced.展开更多
A new method for predicting the trend of displacement evolution of surroundingrock was presented in this paper.According to the nonlinear characteristics of displace-ment time series of underground engineering surroun...A new method for predicting the trend of displacement evolution of surroundingrock was presented in this paper.According to the nonlinear characteristics of displace-ment time series of underground engineering surrounding rock,based on phase spacereconstruction theory and the powerful nonlinear mapping ability of support vector ma-chines,the information offered by the time series datum sets was fully exploited and thenon-linearity of the displacement evolution system of surrounding rock was well described.The example suggests that the methods based on phase space reconstruction and modi-fied v-SVR algorithm are very accurate,and the study can help to build the displacementforecast system to analyze the stability of underground engineering surrounding rock.展开更多
Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environme...Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environment.At present,the monitoring method of seawater pH has been matured.However,how to accurately predict future changes has been lacking effective solutions.Based on this,the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction(ICPBGA)is proposed to achieve seawater pH prediction.To verify the validity of this model,pH data of two monitoring sites in the coastal sea area of Beihai,China are selected to verify the effect.At the same time,the ICPBGA model is compared with other excellent models for predicting chaotic time series,and root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2)are used as performance evaluation indicators.The R2 of the ICPBGA model at Sites 1 and 2 are above 0.9,and the prediction errors are also the smallest.The results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction effect.The prediction method in this paper can be further expanded and used to predict other marine environmental indicators.展开更多
Epilepsy is a chronic neurological disorder that affects the function of the brain in people of all ages.It manifests in the electroencephalogram(EEG) signal which records the electrical activity of the brain.Various ...Epilepsy is a chronic neurological disorder that affects the function of the brain in people of all ages.It manifests in the electroencephalogram(EEG) signal which records the electrical activity of the brain.Various image processing,signal processing,and machine-learning based techniques are employed to analyze epilepsy,using spatial and temporal features.The nervous system that generates the EEG signal is considered nonlinear and the EEG signals exhibit chaotic behavior.In order to capture these nonlinear dynamics,we use reconstructed phase space(RPS) representation of the signal.Earlier studies have primarily addressed seizure detection as a binary classification(normal vs.ictal) problem and rarely as a ternary class(normal vs.interictal vs.ictal)problem.We employ transfer learning on a pre-trained deep neural network model and retrain it using RPS images of the EEG signal.The classification accuracy of the model for the binary classes is(98.5±1.5)% and(95±2)% for the ternary classes.The performance of the convolution neural network(CNN) model is better than the other existing statistical approach for all performance indicators such as accuracy,sensitivity,and specificity.The result of the proposed approach shows the prospect of employing RPS images with CNN for predicting epileptic seizures.展开更多
The neutral network forecasting model based on the phase space reconstruction was proposed. First, through reconstructing the phase space, the time series of single variable was done excursion and expanded into multi-...The neutral network forecasting model based on the phase space reconstruction was proposed. First, through reconstructing the phase space, the time series of single variable was done excursion and expanded into multi- dimension series which included the ergodic information and more rich information could be excavated. Then, on the basis of the embedding dimension of the time series, the structure form of neutral network was constructed, of which the node number in input layer was the embedding dimension of the time series minus 1, and the node number in output layers was 1. Finally, as an example, the model was applied for water yield of mine forecasting. The result shows that the model has good fitting accuracy and forecasting precision.展开更多
预测车轮的磨耗状态对合理制定维护计划、保障动车组列车安全运行具有重要意义。动车组车轮磨耗属复杂非线性问题,引入机器学习和大数据技术对准确预测车轮磨耗具有显著作用。为预测动车组车轮磨耗,提出了一种改进的带外源输入的非线性...预测车轮的磨耗状态对合理制定维护计划、保障动车组列车安全运行具有重要意义。动车组车轮磨耗属复杂非线性问题,引入机器学习和大数据技术对准确预测车轮磨耗具有显著作用。为预测动车组车轮磨耗,提出了一种改进的带外源输入的非线性自回归神经网络(nonlinear auto-regressive model with eXogenous inputs neural network,NARXNN)模型。首先,依据采集的动车组车轮磨耗3年连续实测数据,运用快速查找密度峰值聚类算法剔除异常数据,并利用二阶拉格朗日多项式插值处理缺失值,进行数据集预处理。然后,通过相空间重构将一维时间序列车轮直径测量数据扩展至多维,挖掘车轮磨耗参数的演变规律,采用主成分分析法提取影响动车组车轮直径参数指标变化的主要成分,构建了车轮磨耗预测模型的数据集样本。在此基础上,设计了一种带外源输入的非线性自回归神经网络NARXNN模型,采用列文伯格−马夸尔特算法(levenberg-marquardt,LM)优化NARXNN模型的权重和阈值,引入正交匹配追踪算法(orthogonal matching pursuit,OMP),以贪婪迭代的方式优化网络模型结构,在保证性能可靠性的同时删除冗余神经元,最终实现预测网络结构的精简。实验结果表明,LM-OMP算法优化的NARXNN神经网络模型结构简单、性能优越,能够快速稳定收敛。建立了未来2至6个月的车轮轮径磨耗预测模型,与其他经典预测模型对比结果表明,运用LM-OMP算法优化的NARXNN神经网络模型在动车组车轮磨耗预测方面具有更高的准确率和稳定性。运用该方法能够较精准地预测车轮磨耗的发展规律,可为动车组车轮的精准维修和科学使用提供参考。展开更多
In view of some courses of the time-varying characteristics processing in the analysis of dam deformation,the paper proposes a new method to analyze the dam time-varying characteristic based on the empirical mode deco...In view of some courses of the time-varying characteristics processing in the analysis of dam deformation,the paper proposes a new method to analyze the dam time-varying characteristic based on the empirical mode decomposition and phase space reconstruction theory.First of all,to reduce the influences on the traditional statistical model from human factors and assure the analysis accuracy,response variables of the time-varying characteristic are obtained by the way of the empirical mode decomposition;and then,a phase plane of those variables is reconstructed to investigate their processing rules.These methods have already been applied to an actual project and the results showed that data interpretation with the assists of empirical mode decomposition and phase space reconstruction is effective in analyzing the perturbations of response variables,explicit in reflecting the entire development process,and valid for obtaining the evolution rules of the time-varying characteristic.This methodology is a powerful technical support for people to further master the rules of dam operation.展开更多
文摘Phase space is one of the most important parameters used to describe beam properties. Computer tomography, as a method for reconstructing phase space and measuring beam emittance, has been used in many accelerators over the past few decades. In this paper, we demonstrate a transverse phase space reconstruction study in the Shanghai soft X-ray free electron laser facility. First,we discuss the basic principles of phase space reconstruction and the advantage of reconstructing beam distribution in normalized phase space. Then, the phase space reconstruction results by different computer tomography methods based on the maximum entropy(MENT) algorithm and the filtered back projection algorithm in normalized phase space are presented. The simulation results indicate that,with proper configuration of the phase advance between adjacent screens, the MENT algorithm is feasible and has good efficiency. The beam emittance and Twiss parameters are also calculated using the reconstructed phase space.
基金Sponsored by the National Eleventh Five year Plan Key Project of Ministry of Science and Technology of China (Grant No. 2006BAJ03A05-05)
文摘To make elevator group control system better follow the change of elevator traffic flow (ETF) in order to adjust the control strategy,the prediction method of support vector machine (SVM) in combination with phase space reconstruction has been proposed for ETF.Firstly,the phase space reconstruction for elevator traffic flow time series (ETFTS) is processed.Secondly,the small data set method is applied to calculate the largest Lyapunov exponent to judge the chaotic property of ETF.Then prediction model of ETFTS based on SVM is founded.Finally,the method is applied to predict the time series for the incoming and outgoing passenger flow respectively using ETF data collected in some building.Meanwhile,it is compared with RBF neural network model.Simulation results show that the trend of factual traffic flow is better followed by predictive traffic flow.SVM algorithm has much better prediction performance.The fitting and prediction of ETF with better effect are realized.
基金Supported by Major State Basic Research Program of China ("973" Program,No. 2011CB610505)Specialized Research Fund for the Doctoral Program of Higher Education (No. 20120032110029)
文摘The degradation process of organosol coated tinplate in beverage was investigated by electrochemical noise (EN) technique combined with morphology characterization.EN data were analyzed using phase space reconstruction theory.With the correlation dimensions obtained from the phase space reconstruction,the chaotic behavior of EN was quantitatively evaluated.The results show that both electrochemical potential noise (EPN) and electrochemical current noise (ECN) have chaotic properties.The correlation dimensions of EPN increase with corrosion extent,while those of ECN seem nearly unchanged.The increased correlation dimensions of EPN during the degradation process are associated with the increased susceptibility to local corrosion.
基金supported by the Natural Science Foundation of Fujian Province of China (Grant Nos. 2010J01210 and T0750008)
文摘In view of the disadvantages of the traditional phase space reconstruction method, this paper presents the method of phase space reconstruction based on the wavelet decomposition and indicates that the wavelet decomposition of chaotic dynamical system is essentially a projection of chaotic attractor on the axes of space opened by the wavelet filter vectors, which corresponds to the time-delayed embedding method of phase space reconstruction proposed by Packard and Takens. The experimental results show that, the structure of dynamical trajectory of chaotic system on the wavelet space is much similar to the original system, and the nonlinear invariants such as correlation dimension, Lyapunov exponent and Kolmogorov entropy are still reserved. It demonstrates that wavelet decomposition is effective for characterizing chaotic dynamical system.
基金Supported by 863 Program of China(2002AA2Z4291) Henan Innovation Project for University Prominent Research Talents(2005KYCX015)Henan Project for University Prominent Talents
文摘Proposed a new method to disclose the complicated non-linearity structure of the water-resource system, introducing chaos theory into the hydrology and water resources field, and combined with the chaos theory and artificial neural networks. Training data construction and networks structure were determined by the phase space reconstruction, and establishing nonlinear relationship of phase points with neural networks, the forecasting model of the resource quantity of the surface water was brought forward. The keystone of the way and the detailed arithmetic of the network training were given. The example shows that the model has highly forecasting precision.
基金Project (No. 50437010) supported by the Key Program of the Na-tional Natural Science Foundation of China
文摘Power Quality (PQ) combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The classification system consists of two parts, namely the feature extraction and the automatic recognition. In the feature extraction stage, Phase Space Reconstruction (PSR), a time series analysis tool, is utilized to construct disturbance signal trajectories. For these trajectories, several indices are proposed to form the feature vectors. Support Vector Machines (SVMs) are then implemented to recognize the different patterns and to evaluate the efficiencies. The types of disturbances discussed include a combination of short-term dis-turbances (voltage sags, swells) and long-term disturbances (flickers, harmonics), as well as their homologous single ones. The feasibilities of the proposed approach are verified by simulation with thousands of PQ events. Comparison studies based on Wavelet Transform (WT) and Artificial Neural Network (ANN) are also reported to show its advantages.
基金Supported by the National Natural Science Foundation of China(51406031)
文摘A new method of nonlinear analysis is established by combining phase space reconstruction and data reduction sub-frequency band wavelet. This method is applied to two types of chaotic dynamic systems(Lorenz and Rssler) to examine the anti-noise ability for complex systems. Results show that the nonlinear dynamic system analysis method resists noise and reveals the internal dynamics of a weak signal from noise pollution. On this basis, the vertical upward gas–liquid two-phase flow in a 2 mm × 0.81 mm small rectangular channel is investigated. The frequency and energy distributions of the main oscillation mode are revealed by analyzing the time–frequency spectra of the pressure signals of different flow patterns. The positive power spectral density of singular-value frequency entropy and the damping ratio are extracted to characterize the evolution of flow patterns and achieve accurate recognition of different vertical upward gas–liquid flow patterns(bubbly flow:100%, slug flow: 92%, churn flow: 96%, annular flow: 100%). The proposed analysis method will enrich the dynamics theory of multi-phase flow in small channel.
基金the National Key R&D Program of China(No.2022YFC2904103)the Key Program of the National Natural Science Foundation of China(No.52034001)+1 种基金the 111 Project(No.B20041)the China National Postdoctoral Program for Innovative Talents(No.BX20230041)。
文摘Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress conditions.Current monitoring data processing methods cannot fully consider the complexity of monitoring objects,the diversity of monitoring methods,and the dynamics of monitoring data.To solve this problem,this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock combinations.The three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress,multipoint displacement meter,and inclinometer.Varied information,such as the stress and displacement of the filling body–surrounding rock combination,was continuously obtained.Combined with the average mutual information method and the false nearest neighbor point method,the phase space of the heterogeneous information of the filling body–surrounding rock combination was then constructed.In this paper,the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock combination.The evaluated distances(ED)revealed a high sensitivity to the stability of the filling body–surrounding rock combination.The new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou Mine.The moments of mutation in these time series were at least 3 months ahead of the roadway return dates.In the ED prediction experiments,the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models(long short-term memory and Transformer).Furthermore,the root-mean-square error distribution of the prediction results peaked at 0.26,thus outperforming the no-prediction method in 70%of the cases.
基金National High Technology Research and Development Program of China(No.2007AA04Z109)Open Research Fund of State Key Laboratory of Digital Manufacturing Equipment and Technology at Huazhong University of Science and Technology,China(No.DMETKF2009006)
文摘In order to manage and control semiconductor wafer fabrication system (SWFS) more effectively,the daily throughput prediction data of wafer fab are often used in the planning and scheduling of SWFS.In this paper,an artificial neural network (ANN) prediction method based on phase space reconstruction (PSR) and ant colony optimization (ACO) is presented,in which the phase space reconstruction theory is used to reconstruct the daily throughput time series,the ANN is used to construct the daily throughput prediction model,and the ACO is used to train the connection weight and bias values of the neural network prediction model.Testing with factory operation data and comparing with the traditional method show that the proposed methodology is effective.
基金Supported by the Key Program of National Natural Science Foundation of China(Nos.61077071,51075349)Program of National Natural Science Foundation of Hebei Province(Nos.F2011203207,F2010001312)
文摘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.
基金Supported by the Naltural Science Foundation of Hunan Province(97JJY1006)Open Foundation of Stalte Key Lab. of Theory and Chief Technology on ISN of Xidian University(991894102)
文摘In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine proper minimum embedding dimension is constructed. This method has a sound theoretical basis and can lead to good result. It can indicate the noise level in the data to be reconstructed, and estimate the reconstruction quality. It is applied to speech signal reconstruction and the generic embedding dimension of speech signals is deduced.
文摘A new method for predicting the trend of displacement evolution of surroundingrock was presented in this paper.According to the nonlinear characteristics of displace-ment time series of underground engineering surrounding rock,based on phase spacereconstruction theory and the powerful nonlinear mapping ability of support vector ma-chines,the information offered by the time series datum sets was fully exploited and thenon-linearity of the displacement evolution system of surrounding rock was well described.The example suggests that the methods based on phase space reconstruction and modi-fied v-SVR algorithm are very accurate,and the study can help to build the displacementforecast system to analyze the stability of underground engineering surrounding rock.
基金The National Natural Science Foundation of China under contract No.62275228the S&T Program of Hebei under contract Nos 19273901D and 20373301Dthe Hebei Natural Science Foundation under contract No.F2020203066.
文摘Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environment.At present,the monitoring method of seawater pH has been matured.However,how to accurately predict future changes has been lacking effective solutions.Based on this,the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction(ICPBGA)is proposed to achieve seawater pH prediction.To verify the validity of this model,pH data of two monitoring sites in the coastal sea area of Beihai,China are selected to verify the effect.At the same time,the ICPBGA model is compared with other excellent models for predicting chaotic time series,and root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2)are used as performance evaluation indicators.The R2 of the ICPBGA model at Sites 1 and 2 are above 0.9,and the prediction errors are also the smallest.The results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction effect.The prediction method in this paper can be further expanded and used to predict other marine environmental indicators.
文摘Epilepsy is a chronic neurological disorder that affects the function of the brain in people of all ages.It manifests in the electroencephalogram(EEG) signal which records the electrical activity of the brain.Various image processing,signal processing,and machine-learning based techniques are employed to analyze epilepsy,using spatial and temporal features.The nervous system that generates the EEG signal is considered nonlinear and the EEG signals exhibit chaotic behavior.In order to capture these nonlinear dynamics,we use reconstructed phase space(RPS) representation of the signal.Earlier studies have primarily addressed seizure detection as a binary classification(normal vs.ictal) problem and rarely as a ternary class(normal vs.interictal vs.ictal)problem.We employ transfer learning on a pre-trained deep neural network model and retrain it using RPS images of the EEG signal.The classification accuracy of the model for the binary classes is(98.5±1.5)% and(95±2)% for the ternary classes.The performance of the convolution neural network(CNN) model is better than the other existing statistical approach for all performance indicators such as accuracy,sensitivity,and specificity.The result of the proposed approach shows the prospect of employing RPS images with CNN for predicting epileptic seizures.
文摘The neutral network forecasting model based on the phase space reconstruction was proposed. First, through reconstructing the phase space, the time series of single variable was done excursion and expanded into multi- dimension series which included the ergodic information and more rich information could be excavated. Then, on the basis of the embedding dimension of the time series, the structure form of neutral network was constructed, of which the node number in input layer was the embedding dimension of the time series minus 1, and the node number in output layers was 1. Finally, as an example, the model was applied for water yield of mine forecasting. The result shows that the model has good fitting accuracy and forecasting precision.
文摘预测车轮的磨耗状态对合理制定维护计划、保障动车组列车安全运行具有重要意义。动车组车轮磨耗属复杂非线性问题,引入机器学习和大数据技术对准确预测车轮磨耗具有显著作用。为预测动车组车轮磨耗,提出了一种改进的带外源输入的非线性自回归神经网络(nonlinear auto-regressive model with eXogenous inputs neural network,NARXNN)模型。首先,依据采集的动车组车轮磨耗3年连续实测数据,运用快速查找密度峰值聚类算法剔除异常数据,并利用二阶拉格朗日多项式插值处理缺失值,进行数据集预处理。然后,通过相空间重构将一维时间序列车轮直径测量数据扩展至多维,挖掘车轮磨耗参数的演变规律,采用主成分分析法提取影响动车组车轮直径参数指标变化的主要成分,构建了车轮磨耗预测模型的数据集样本。在此基础上,设计了一种带外源输入的非线性自回归神经网络NARXNN模型,采用列文伯格−马夸尔特算法(levenberg-marquardt,LM)优化NARXNN模型的权重和阈值,引入正交匹配追踪算法(orthogonal matching pursuit,OMP),以贪婪迭代的方式优化网络模型结构,在保证性能可靠性的同时删除冗余神经元,最终实现预测网络结构的精简。实验结果表明,LM-OMP算法优化的NARXNN神经网络模型结构简单、性能优越,能够快速稳定收敛。建立了未来2至6个月的车轮轮径磨耗预测模型,与其他经典预测模型对比结果表明,运用LM-OMP算法优化的NARXNN神经网络模型在动车组车轮磨耗预测方面具有更高的准确率和稳定性。运用该方法能够较精准地预测车轮磨耗的发展规律,可为动车组车轮的精准维修和科学使用提供参考。
基金supported by the National Science and Technology Support Plan of China(Grant Nos.2006BAC14B03,2008BAB29B06,2008BAB29B03)National Natural Science Foundation of China(Grant Nos.50909041,50879024,50809025)+2 种基金Science and Technology Support Project of China Hydropower Engineering Consulting Group Co.(Grant No.CHC-KJ-2007-02)Special Scientific Research Fund of Doctor Subjects of Colleges and Universities(Grant No.20070294023)Jiangsu Province 333Training Highlevel Talents Projects(Grant Nos.2017-B08037)
文摘In view of some courses of the time-varying characteristics processing in the analysis of dam deformation,the paper proposes a new method to analyze the dam time-varying characteristic based on the empirical mode decomposition and phase space reconstruction theory.First of all,to reduce the influences on the traditional statistical model from human factors and assure the analysis accuracy,response variables of the time-varying characteristic are obtained by the way of the empirical mode decomposition;and then,a phase plane of those variables is reconstructed to investigate their processing rules.These methods have already been applied to an actual project and the results showed that data interpretation with the assists of empirical mode decomposition and phase space reconstruction is effective in analyzing the perturbations of response variables,explicit in reflecting the entire development process,and valid for obtaining the evolution rules of the time-varying characteristic.This methodology is a powerful technical support for people to further master the rules of dam operation.
