The traditional least squares support vector regression（LS-SVR）model,using cross validation to determine the regularization parameter and kernel parameter,is time-consuming.We propose a Bayesian evidence framework t...The traditional least squares support vector regression（LS-SVR）model,using cross validation to determine the regularization parameter and kernel parameter,is time-consuming.We propose a Bayesian evidence framework to infer the LS-SVR model parameters.Three levels Bayesian inferences are used to determine the model parameters,regularization hyper-parameters and tune the nuclear parameters by model comparison.On this basis,we established Bayesian LS-SVR time-series gas forecasting models and provide steps for the algorithm.The gas outburst data of a Hebi 10th mine working face is used to validate the model.The optimal embedding dimension and delay time of the time series were obtained by the smallest differential entropy method.Finally,within a MATLAB7.1 environment,we used actual coal gas data to compare the traditional LS-SVR and the Bayesian LS-SVR with LS-SVMlab1.5 Toolbox simulation.The results show that the Bayesian framework of an LS-SVR significantly improves the speed and accuracy of the forecast.展开更多
The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasti...The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasting analysis for the spectral analysis data co llected from aero-engines. In the oil condition monitoring field of mechanical equipment, the use of the method of time-series analysis has rarely been report ed. As indicated in the satisfactory example, a practical method for condition m onitoring and fault forecasting of mechanical equipment has been achieved.展开更多
In order to compensate for the deficiency of present methods of monitoring plane displacement in similarity model tests,such as inadequate real-time monitoring and more manual intervention,an effective monitoring meth...In order to compensate for the deficiency of present methods of monitoring plane displacement in similarity model tests,such as inadequate real-time monitoring and more manual intervention,an effective monitoring method was proposed in this study,and the major steps of the monitoring method include:firstly,time-series images of the similarity model in the test were obtained by a camera,and secondly,measuring points marked as artificial targets were automatically tracked and recognized from time-series images.Finally,the real-time plane displacement field was calculated by the fixed magnification between objects and images under the specific conditions.And then the application device of the method was designed and tested.At the same time,a sub-pixel location method and a distortion error model were used to improve the measuring accuracy.The results indicate that this method may record the entire test,especially the detailed non-uniform deformation and sudden deformation.Compared with traditional methods this method has a number of advantages,such as greater measurement accuracy and reliability,less manual intervention,higher automation,strong practical properties,much more measurement information and so on.展开更多
A new real-time model based on parallel time-series mining is proposed to improve the accuracy and efficiency of the network intrusion detection systems. In this model, multidimensional dataset is constructed to descr...A new real-time model based on parallel time-series mining is proposed to improve the accuracy and efficiency of the network intrusion detection systems. In this model, multidimensional dataset is constructed to describe network events, and sliding window updating algorithm is used to maintain network stream. Moreover, parallel frequent patterns and frequent episodes mining algorithms are applied to implement parallel time-series mining engineer which can intelligently generate rules to distinguish intrusions from normal activities. Analysis and study on the basis of DAWNING 3000 indicate that this parallel time-series mining-based model provides a more accurate and efficient way to building real-time NIDS.展开更多
Background:Climate change profoundly shapes the population health at the global scale.However,there was still insufficient and inconsistent evidence for the association between heat exposure and chronic kidney disease...Background:Climate change profoundly shapes the population health at the global scale.However,there was still insufficient and inconsistent evidence for the association between heat exposure and chronic kidney disease(CKD).Methods:In the present study,we studied the association of heat exposure with hospitalizations for cause-specific CKD using a national inpatient database in China during the study period of hot season from 2015 to 2018.Standard time-series regression models and random-effects Meta-analysis were developed to estimate the city-specific and national averaged associations at a 7 lag-day span,respectively.Results:A total of 768,129 hospitalizations for CKD was recorded during the study period.The results showed that higher temperature was associated with elevated risk of hospitalizations for CKD,especially in sub-tropical cities.With a 1℃ increase in daily mean temperature,the cumulative relative risks(RR)over lag 0-7 d were 1.008[95% confidence interval(CI)1.003-1.012]for nationwide.The attributable fraction of CKD hospitalizations due to high temperatures was 5.50%.Stronger associations were observed among younger patients and those with obstructive nephropathy.Our study also found that exposure to heatwaves was associated with added risk of hospitalizations for CKD compared to non-heatwave days(RR=1.116,95%CI 1.069-1.166)above the effect of daily mean temperature.Conclusions:Short-term heat exposure may increase the risk of hospitalization for CKD.Our findings provide insights into the health effects of climate change and suggest the necessity of guided protection strategies against the adverse effects of high temperatures.展开更多
Time-series anomaly detection plays a crucial role in the operation of offshore wind turbines.Various wind turbine monitoring systems rely on time-series data to monitor and identify anomalies in real-time,as well as ...Time-series anomaly detection plays a crucial role in the operation of offshore wind turbines.Various wind turbine monitoring systems rely on time-series data to monitor and identify anomalies in real-time,as well as to initiate early warning processes.However,for offshore wind turbines with a high data density,conventional methods have high computational overhead in detecting anomalies while failing to accurately detect anomalies due to variations in data scales.To address this challenge,we propose an efficient anomaly detection method with contrastive learning,called Hawkeye.Hawkeye is based on residual clustering,an unsupervised anomaly detection method for multivariate time-series data.To ensure accurate anomaly detection,a trend-capturing prediction module is also combined with an automatic labeling module.As a result,the most common information can be learned from multivariate time-series data to reconstruct data trends.By evaluating Hawkeye on public datasets and real-world offshore wind turbine operational datasets,the results show that Hawkeye’s F_(1)-score improves by an average of 14% compared with Isolation Forest,and its size shrinks by up to 11.5 times on the largest dataset compared with other methods.The proposed Hawkeye is potential to real-time monitoring and early warning systems for wind turbines,accelerating the development of intelligent operation and maintenance.展开更多
Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produce...Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produced during collecting information and summarizing original data of traffic flow, cause large errors in the traffic flow forecasting results. This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction(WT-FCTGN). To eliminate the potential noise and strengthen the potential traffic trend in the data, we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data. The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic, and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow. The performance of the WT-FCTGN model is validated on the public Pe MS data set. The experimental results show that the WT-FCTGN model has higher accuracy, and its mean absolute error(MAE), mean absolute percentage error(MAPE) and root mean square error(RMSE) are obviously lower than those of the other algorithms. The robust experimental results prove that the WT-FCTGN model has good anti-noise ability.展开更多
Satellite communication systems(SCS) operating on frequency bands above 10 GHz are sensitive to atmosphere physical phenomena, especially rain attenuation. To evaluate impairments in satellite performance, stochastic ...Satellite communication systems(SCS) operating on frequency bands above 10 GHz are sensitive to atmosphere physical phenomena, especially rain attenuation. To evaluate impairments in satellite performance, stochastic dynamic modeling(SDM) is considered as an effective way to predict real-time satellite channel fading caused by rain. This article carries out a survey of SDM using stochastic differential equations(SDEs) currently in the literature. Special attention is given to the different input characteristics of each model to satisfy specific local conditions. Future research directions in SDM are also suggested in this paper.展开更多
Decentralized machine learning frameworks,e.g.,federated learning,are emerging to facilitate learning with medical data under privacy protection.It is widely agreed that the establishment of an accurate and robust med...Decentralized machine learning frameworks,e.g.,federated learning,are emerging to facilitate learning with medical data under privacy protection.It is widely agreed that the establishment of an accurate and robust medical learning model requires a large number of continuous synchronous monitoring data of patients from various types of monitoring facilities.However,the clinic monitoring data are usually sparse and imbalanced with errors and time irregularity,leading to inaccurate risk prediction results.To address this issue,this paper designs a medical data resampling and balancing scheme for federated learning to eliminate model biases caused by sample imbalance and provide accurate disease risk prediction on multi-center medical data.Experimental results on a real-world clinical database MIMIC-Ⅳ demonstrate that the proposed method can improve AUC(the area under the receiver operating characteristic) from 50.1% to 62.8%,with a significant performance improvement of accuracy from 76.8% to 82.2%,compared to a vanilla federated learning artificial neural network(ANN).Moreover,we increase the model’s tolerance for missing data from 20% to 50% compared with a stand-alone baseline model.展开更多
We study the statistical properties of volatility of price fluctuation for the Hang-Seng index in the Hong Kong stock market, they are measured by locally averaging over a time window T, the absolute value of price ch...We study the statistical properties of volatility of price fluctuation for the Hang-Seng index in the Hong Kong stock market, they are measured by locally averaging over a time window T, the absolute value of price change over a short time interval Δt. The data include minute-by-minute records of the Hang-Seng index from 3 January 1994 to 28 May 1997. We find that the cumulative distribution of the volatility is consistent with the asymptotic power-law behaviour, characterized by the power exponent μ= 2.12 ± 0.04, different from that found in the previous studies as μ≈3. The volatility distribution remains the same asymptotic power-law behaviour for the time scales from T = 10 rain to T - 80 rain. Furthermore, we investigate the volatility correlations by using the power spectrum analysis and detrended fluctuation analysis. Both the methods show a long-range power-law decay with the exponent α=0.636±0.002.展开更多
We investigate a set of complex heart rate time series from healthy human in different behaviour states with the detrended fluctuation analysis and diffusion entropy （DE） method. It is proposed that the scaling prop...We investigate a set of complex heart rate time series from healthy human in different behaviour states with the detrended fluctuation analysis and diffusion entropy （DE） method. It is proposed that the scaling properties are influenced by behaviour states. The memory detected by DE exhibits an approximately same pattern after a detrending procedure. Both of them demonstrate the long-range strong correlations in heart rate. These findings may be helpful to understand the underlying dynamical evolution process in the heart rate control system, as well as to model the cardiac dynamic process.展开更多
Time series classification(TSC)has attracted a lot of attention for time series data mining tasks and has been applied in various fields.With the success of deep learning(DL)in computer vision recognition,people are s...Time series classification(TSC)has attracted a lot of attention for time series data mining tasks and has been applied in various fields.With the success of deep learning(DL)in computer vision recognition,people are starting to use deep learning to tackle TSC tasks.Quantum neural networks(QNN)have recently demonstrated their superiority over traditional machine learning in methods such as image processing and natural language processing,but research using quantum neural networks to handle TSC tasks has not received enough attention.Therefore,we proposed a learning framework based on multiple imaging and hybrid QNN(MIHQNN)for TSC tasks.We investigate the possibility of converting 1D time series to 2D images and classifying the converted images using hybrid QNN.We explored the differences between MIHQNN based on single time series imaging and MIHQNN based on the fusion of multiple time series imaging.Four quantum circuits were also selected and designed to study the impact of quantum circuits on TSC tasks.We tested our method on several standard datasets and achieved significant results compared to several current TSC methods,demonstrating the effectiveness of MIHQNN.This research highlights the potential of applying quantum computing to TSC and provides the theoretical and experimental background for future research.展开更多
Complex stimuli are used to probe the response properties of the chicken＇s retinal ganglion cells （GCs）. The correlation dimension method and the nonlinear forecasting method are applied to detect the determinism i...Complex stimuli are used to probe the response properties of the chicken＇s retinal ganglion cells （GCs）. The correlation dimension method and the nonlinear forecasting method are applied to detect the determinism in the firing activities of the retinal GCs during response to complex stimuli. The inter-spike interval （ISI） series and the first difference of the ISI （DISI） series are analysed. Two conclusions are drawn. Firstly, the first difference operation of the ISI series makes it comparatively easier for determinism detection in the firing activities of retinal GCs. Secondly, the nonlinear forecasting method is more efficient and reliable than the correlation dimension method for determinism detection.展开更多
In the reconstructed phase space, a novel local linear prediction model is proposed to predict chaotic time series. The parameters of the proposed model take the values that are different from those of the phase space...In the reconstructed phase space, a novel local linear prediction model is proposed to predict chaotic time series. The parameters of the proposed model take the values that are different from those of the phase space reconstruction. We propose a criterion based on prediction error to determine the optimal parameters of the proposed model. The simulation results show that the proposed model can effectively make one-step and multistep prediction for chaotic time series, and the one-step and multi-step prediction accuracy of the proposed model is superior to that of the traditional local linear prediction.展开更多
基金Financial support for this work,provided by the National Natural Science Foundation of China(No.60974126)the Natural Science Foundation of Jiangsu Province(No.BK2009094)
文摘The traditional least squares support vector regression（LS-SVR）model,using cross validation to determine the regularization parameter and kernel parameter,is time-consuming.We propose a Bayesian evidence framework to infer the LS-SVR model parameters.Three levels Bayesian inferences are used to determine the model parameters,regularization hyper-parameters and tune the nuclear parameters by model comparison.On this basis,we established Bayesian LS-SVR time-series gas forecasting models and provide steps for the algorithm.The gas outburst data of a Hebi 10th mine working face is used to validate the model.The optimal embedding dimension and delay time of the time series were obtained by the smallest differential entropy method.Finally,within a MATLAB7.1 environment,we used actual coal gas data to compare the traditional LS-SVR and the Bayesian LS-SVR with LS-SVMlab1.5 Toolbox simulation.The results show that the Bayesian framework of an LS-SVR significantly improves the speed and accuracy of the forecast.
文摘The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasting analysis for the spectral analysis data co llected from aero-engines. In the oil condition monitoring field of mechanical equipment, the use of the method of time-series analysis has rarely been report ed. As indicated in the satisfactory example, a practical method for condition m onitoring and fault forecasting of mechanical equipment has been achieved.
基金provided by the Program for New Century Excellent Talents in University (No. NCET-06-0477)the Independent Research Project of the State Key Laboratory of Coal Resources and Mine Safety of China University of Mining and Technology (No. SKLCRSM09X01)the Fundamental Research Funds for the Central Universities
文摘In order to compensate for the deficiency of present methods of monitoring plane displacement in similarity model tests,such as inadequate real-time monitoring and more manual intervention,an effective monitoring method was proposed in this study,and the major steps of the monitoring method include:firstly,time-series images of the similarity model in the test were obtained by a camera,and secondly,measuring points marked as artificial targets were automatically tracked and recognized from time-series images.Finally,the real-time plane displacement field was calculated by the fixed magnification between objects and images under the specific conditions.And then the application device of the method was designed and tested.At the same time,a sub-pixel location method and a distortion error model were used to improve the measuring accuracy.The results indicate that this method may record the entire test,especially the detailed non-uniform deformation and sudden deformation.Compared with traditional methods this method has a number of advantages,such as greater measurement accuracy and reliability,less manual intervention,higher automation,strong practical properties,much more measurement information and so on.
文摘A new real-time model based on parallel time-series mining is proposed to improve the accuracy and efficiency of the network intrusion detection systems. In this model, multidimensional dataset is constructed to describe network events, and sliding window updating algorithm is used to maintain network stream. Moreover, parallel frequent patterns and frequent episodes mining algorithms are applied to implement parallel time-series mining engineer which can intelligently generate rules to distinguish intrusions from normal activities. Analysis and study on the basis of DAWNING 3000 indicate that this parallel time-series mining-based model provides a more accurate and efficient way to building real-time NIDS.
基金This study was supported by the National Natural Science Foundation of China(82003529,72125009)the National Key Research and Development Program of the Ministry of Science and Technology of China(2019YFC2005000)+4 种基金the Chinese Scientific and Technical Innovation Project 2030(2018AAA0102100)the National High Level Hospital Clinical Research Funding(“Star of Outlook”Scientific Research Project of Peking University First Hospital,2022XW06)the CAMS Innovation Fund for Medical Sciences(2019-I2M-5-046)the Young Elite Scientists Sponsorship Program by CAST(2022QNRC001)the PKU-Baidu Fund(2020BD004,2020BD005 and 2020BD032).
文摘Background:Climate change profoundly shapes the population health at the global scale.However,there was still insufficient and inconsistent evidence for the association between heat exposure and chronic kidney disease(CKD).Methods:In the present study,we studied the association of heat exposure with hospitalizations for cause-specific CKD using a national inpatient database in China during the study period of hot season from 2015 to 2018.Standard time-series regression models and random-effects Meta-analysis were developed to estimate the city-specific and national averaged associations at a 7 lag-day span,respectively.Results:A total of 768,129 hospitalizations for CKD was recorded during the study period.The results showed that higher temperature was associated with elevated risk of hospitalizations for CKD,especially in sub-tropical cities.With a 1℃ increase in daily mean temperature,the cumulative relative risks(RR)over lag 0-7 d were 1.008[95% confidence interval(CI)1.003-1.012]for nationwide.The attributable fraction of CKD hospitalizations due to high temperatures was 5.50%.Stronger associations were observed among younger patients and those with obstructive nephropathy.Our study also found that exposure to heatwaves was associated with added risk of hospitalizations for CKD compared to non-heatwave days(RR=1.116,95%CI 1.069-1.166)above the effect of daily mean temperature.Conclusions:Short-term heat exposure may increase the risk of hospitalization for CKD.Our findings provide insights into the health effects of climate change and suggest the necessity of guided protection strategies against the adverse effects of high temperatures.
基金supported by Shanghai Electric Power Energy Technology Co.,Ltd.2023 Science and Technology Project under Grant No.33019006220801.
文摘Time-series anomaly detection plays a crucial role in the operation of offshore wind turbines.Various wind turbine monitoring systems rely on time-series data to monitor and identify anomalies in real-time,as well as to initiate early warning processes.However,for offshore wind turbines with a high data density,conventional methods have high computational overhead in detecting anomalies while failing to accurately detect anomalies due to variations in data scales.To address this challenge,we propose an efficient anomaly detection method with contrastive learning,called Hawkeye.Hawkeye is based on residual clustering,an unsupervised anomaly detection method for multivariate time-series data.To ensure accurate anomaly detection,a trend-capturing prediction module is also combined with an automatic labeling module.As a result,the most common information can be learned from multivariate time-series data to reconstruct data trends.By evaluating Hawkeye on public datasets and real-world offshore wind turbine operational datasets,the results show that Hawkeye’s F_(1)-score improves by an average of 14% compared with Isolation Forest,and its size shrinks by up to 11.5 times on the largest dataset compared with other methods.The proposed Hawkeye is potential to real-time monitoring and early warning systems for wind turbines,accelerating the development of intelligent operation and maintenance.
基金The Science and Technology Research and Development Program Project of China Railway Group Ltd provided funding for this study(Project Nos.2020-Special-02 and 2021Special-08)。
文摘Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produced during collecting information and summarizing original data of traffic flow, cause large errors in the traffic flow forecasting results. This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction(WT-FCTGN). To eliminate the potential noise and strengthen the potential traffic trend in the data, we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data. The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic, and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow. The performance of the WT-FCTGN model is validated on the public Pe MS data set. The experimental results show that the WT-FCTGN model has higher accuracy, and its mean absolute error(MAE), mean absolute percentage error(MAPE) and root mean square error(RMSE) are obviously lower than those of the other algorithms. The robust experimental results prove that the WT-FCTGN model has good anti-noise ability.
基金supported by the National Natural Science Foundation of China (Grant No.91338201)
文摘Satellite communication systems(SCS) operating on frequency bands above 10 GHz are sensitive to atmosphere physical phenomena, especially rain attenuation. To evaluate impairments in satellite performance, stochastic dynamic modeling(SDM) is considered as an effective way to predict real-time satellite channel fading caused by rain. This article carries out a survey of SDM using stochastic differential equations(SDEs) currently in the literature. Special attention is given to the different input characteristics of each model to satisfy specific local conditions. Future research directions in SDM are also suggested in this paper.
基金supported by Hubei Provincial Development and Reform Commission Program"Hubei Big Data Analysis Platform and Intelligent Service Project for Medical and Health"。
文摘Decentralized machine learning frameworks,e.g.,federated learning,are emerging to facilitate learning with medical data under privacy protection.It is widely agreed that the establishment of an accurate and robust medical learning model requires a large number of continuous synchronous monitoring data of patients from various types of monitoring facilities.However,the clinic monitoring data are usually sparse and imbalanced with errors and time irregularity,leading to inaccurate risk prediction results.To address this issue,this paper designs a medical data resampling and balancing scheme for federated learning to eliminate model biases caused by sample imbalance and provide accurate disease risk prediction on multi-center medical data.Experimental results on a real-world clinical database MIMIC-Ⅳ demonstrate that the proposed method can improve AUC(the area under the receiver operating characteristic) from 50.1% to 62.8%,with a significant performance improvement of accuracy from 76.8% to 82.2%,compared to a vanilla federated learning artificial neural network(ANN).Moreover,we increase the model’s tolerance for missing data from 20% to 50% compared with a stand-alone baseline model.
基金Supported by the National Natural Science Foundation of China under Grant Nos 70471033, 10472116, 10532060, 10547004, 70571074, and 70571075.
文摘We study the statistical properties of volatility of price fluctuation for the Hang-Seng index in the Hong Kong stock market, they are measured by locally averaging over a time window T, the absolute value of price change over a short time interval Δt. The data include minute-by-minute records of the Hang-Seng index from 3 January 1994 to 28 May 1997. We find that the cumulative distribution of the volatility is consistent with the asymptotic power-law behaviour, characterized by the power exponent μ= 2.12 ± 0.04, different from that found in the previous studies as μ≈3. The volatility distribution remains the same asymptotic power-law behaviour for the time scales from T = 10 rain to T - 80 rain. Furthermore, we investigate the volatility correlations by using the power spectrum analysis and detrended fluctuation analysis. Both the methods show a long-range power-law decay with the exponent α=0.636±0.002.
基金Supported by the National Science Foundation of China under Grant Nos 70571075, 70571074 and 10635040, the Foundation for graduate students of University of Science and Technology of China under Grant No KD2006046, and the Foundation of Wang Kuan Cheng.
文摘We investigate a set of complex heart rate time series from healthy human in different behaviour states with the detrended fluctuation analysis and diffusion entropy （DE） method. It is proposed that the scaling properties are influenced by behaviour states. The memory detected by DE exhibits an approximately same pattern after a detrending procedure. Both of them demonstrate the long-range strong correlations in heart rate. These findings may be helpful to understand the underlying dynamical evolution process in the heart rate control system, as well as to model the cardiac dynamic process.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.61772295 and 61572270)the PHD foundation of Chongqing Normal University (Grant No.19XLB003)Chongqing Technology Foresight and Institutional Innovation Project (Grant No.cstc2021jsyjyzysbAX0011)。
文摘Time series classification(TSC)has attracted a lot of attention for time series data mining tasks and has been applied in various fields.With the success of deep learning(DL)in computer vision recognition,people are starting to use deep learning to tackle TSC tasks.Quantum neural networks(QNN)have recently demonstrated their superiority over traditional machine learning in methods such as image processing and natural language processing,but research using quantum neural networks to handle TSC tasks has not received enough attention.Therefore,we proposed a learning framework based on multiple imaging and hybrid QNN(MIHQNN)for TSC tasks.We investigate the possibility of converting 1D time series to 2D images and classifying the converted images using hybrid QNN.We explored the differences between MIHQNN based on single time series imaging and MIHQNN based on the fusion of multiple time series imaging.Four quantum circuits were also selected and designed to study the impact of quantum circuits on TSC tasks.We tested our method on several standard datasets and achieved significant results compared to several current TSC methods,demonstrating the effectiveness of MIHQNN.This research highlights the potential of applying quantum computing to TSC and provides the theoretical and experimental background for future research.
基金Supported by the National Basic Research Programme of China under Grant No 2005CB724301, the National Natural Science Foundation of China under Grant No 60775034.
文摘Complex stimuli are used to probe the response properties of the chicken＇s retinal ganglion cells （GCs）. The correlation dimension method and the nonlinear forecasting method are applied to detect the determinism in the firing activities of the retinal GCs during response to complex stimuli. The inter-spike interval （ISI） series and the first difference of the ISI （DISI） series are analysed. Two conclusions are drawn. Firstly, the first difference operation of the ISI series makes it comparatively easier for determinism detection in the firing activities of retinal GCs. Secondly, the nonlinear forecasting method is more efficient and reliable than the correlation dimension method for determinism detection.
基金Supported by the Scientific Research Foundation for the Returned 0verseas Chinese Scholars of China under Grant No 2004.176.4, and the Natural Science Foundation of Shandong Province under Grant No Z2004G01.
文摘In the reconstructed phase space, a novel local linear prediction model is proposed to predict chaotic time series. The parameters of the proposed model take the values that are different from those of the phase space reconstruction. We propose a criterion based on prediction error to determine the optimal parameters of the proposed model. The simulation results show that the proposed model can effectively make one-step and multistep prediction for chaotic time series, and the one-step and multi-step prediction accuracy of the proposed model is superior to that of the traditional local linear prediction.
