Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems.The aim of this paper is to provide a model based on neural networks(NNs)for multi-step-ahead traffi...Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems.The aim of this paper is to provide a model based on neural networks(NNs)for multi-step-ahead traffic prediction.NNs'dependency on parameter setting is the major challenge in using them as a predictor.Given the fact that the best combination of NN parameters results in the minimum error of predicted output,the main problem is NN optimization.So,it is viable to set the best combination of the parameters according to a specific traffic behavior.On the other hand,an automatic method—which is applicable in general cases—is strongly desired to set appropriate parameters for neural networks.This paper defines a self-adjusted NN using the non-dominated sorting genetic algorithm II(NSGA-II)as a multi-objective optimizer for short-term prediction.NSGA-II is used to optimize the number of neurons in the first and second layers of the NN,learning ratio and slope of the activation function.This model addresses the challenge of optimizing a multi-output NN in a self-adjusted way.Performance of the developed network is evaluated by application to both univariate and multivariate traffic flow data from an urban highway.Results are analyzed based on the performance measures,showing that the genetic algorithm tunes the NN as well without any manually pre-adjustment.The achieved prediction accuracy is calculated with multiple measures such as the root mean square error(RMSE),and the RMSE value is 10 and 12 in the best configuration of the proposed model for single and multi-step-ahead traffic flow prediction,respectively.展开更多
Study on solving nonlinear least squares adjustment by parameters is one of the most important and new subjects in modern surveying and mapping field . Many researchers have done a lot of work and gained some solving ...Study on solving nonlinear least squares adjustment by parameters is one of the most important and new subjects in modern surveying and mapping field . Many researchers have done a lot of work and gained some solving methods. These methods mainly include iterative algorithms and direct algorithms mainly. The former searches some methods of rapid convergence based on which surveying adjustment is a kind of problem of nonlinear programming. Among them the iterative algorithms of the most in common use are the Gauss-Newton method, damped least quares, quasi-Newton method and some mutations etc. Although these methods improved the quantity of the observation results to a certain degree, and increased the accuracy of the adjustment results, what we want is whether the initial values of unknown parameters are close to their real values. Of course, the model of the latter has better degree in linearity, that is to say, they nearly have the meaning of deeper theories researches. This paper puts forward a kind of method of solving the problems of nonlinear least squares adjustment by parameters based on neural network theory, and studies its stability and convergency. The results of calculating of living example indicate the method acts well for solving parameters problems by nonlinear least squares adjustment without giving exact approximation of parameters.展开更多
A new gravity base network in the south of the Tibetan Plateau was established with a FG5X absolute gravimeter and three CG-6 gravimeters.The gravity base network consists of 10 absolute gravity points and 17 relative...A new gravity base network in the south of the Tibetan Plateau was established with a FG5X absolute gravimeter and three CG-6 gravimeters.The gravity base network consists of 10 absolute gravity points and 17 relative gravity points.Processing of the absolute data,pre-processing of the relative data and gravity network adjustment model are briefly described.Based a constrained weighted least squares,the combined adjustment of absolute and relative gravity measurements results in the gravity values with a precision of about±4.1μGal.展开更多
Electroencephalographic studies using graph theoretic analysis have found aberrations in functional connectivity in children with developmental dyslexia.However,how the training with visual tasks can change the functi...Electroencephalographic studies using graph theoretic analysis have found aberrations in functional connectivity in children with developmental dyslexia.However,how the training with visual tasks can change the functional connectivity of the semantic network in developmental dyslexia is still unclear.We looked for differences in local and global topological properties of functional networks between 21 healthy controls and 22 dyslexic children(8–9 years old)before and after training with visual tasks in this prospective case-control study.The minimum spanning tree method was used to construct the subjects’brain networks in multiple electroencephalographic frequency ranges during a visual word/pseudoword discrimination task.We found group differences in the theta,alpha,beta and gamma bands for four graph measures suggesting a more integrated network topology in dyslexics before the training compared to controls.After training,the network topology of dyslexic children had become more segregated and similar to that of the controls.In theθ,αandβ1-frequency bands,compared to the controls,the pre-training dyslexics exhibited a reduced degree and betweenness centrality of the left anterior temporal and parietal regions.The simultaneous appearance in the left hemisphere of hubs in temporal and parietal(α,β1),temporal and superior frontal cortex(θ,α),parietal and occipitotemporal cortices(β1),identified in the networks of normally developing children was not present in the brain networks of dyslexics.After training,the hub distribution for dyslexics in the theta and beta1 bands had become similar to that of the controls.In summary,our findings point to a less efficient network configuration in dyslexics compared to a more optimal global organization in the controls.This is the first study to investigate the topological organization of functional brain networks of Bulgarian dyslexic children.Approval for the study was obtained from the Ethics Committee of the Institute of Neurobiology and the Institute for Population and Human Studies,Bulgarian Academy of Sciences(approval No.02-41/12.07.2019)on March 28,2017,and the State Logopedic Center and the Ministry of Education and Science(approval No.09-69/14.03.2017)on July 12,2019.展开更多
In the strip rolling process, shape control system possesses the characteristics of nonlinearity, strong coupling, time delay and time variation. Based on self adapting Elman dynamic recursion network prediction model...In the strip rolling process, shape control system possesses the characteristics of nonlinearity, strong coupling, time delay and time variation. Based on self adapting Elman dynamic recursion network prediction model, the fuzzy control method was used to control the shape on four-high cold mill. The simulation results showed that the system can be applied to real time on line control of the shape.展开更多
Focused on various BP algorithms with variable learning rate based on network system error gradient, a modified learning strategy for training non-linear network models is developed with both the incremental and the d...Focused on various BP algorithms with variable learning rate based on network system error gradient, a modified learning strategy for training non-linear network models is developed with both the incremental and the decremental factors of network learning rate being adjusted adaptively and dynamically. The golden section law is put forward to build a relationship between the network training parameters, and a series of data from an existing model is used to train and test the network parameters. By means of the evaluation of network performance in respect to convergent speed and predicting precision, the effectiveness of the proposed learning strategy can be illustrated.展开更多
Gravity observations adjustment is studied having in view to take full advantage of the modern technology of gravity measurement. We present here results of a test performed with the mathematical model proposed by our...Gravity observations adjustment is studied having in view to take full advantage of the modern technology of gravity measurement. We present here results of a test performed with the mathematical model proposed by our group, on the adjustment of gravity observations carried out on network design. Additionally, considering the recent improvement on instrumental technology in gravimetry, that model was modified to take into account possible nonlinear local datum scale factors, in a 1900 mGal range network, and to check its significance for microgal precision measurements. The data set of the Brazilian Fundamental Gravity Network was used as case study. With about 1900 mGal gravity range and 11 control stations the Brazilian Fundamental Gravity Network (BFGN) was used as case study. It was established mainly with the use of LaCoste & Romberg, model G, gravimeters and new additional observations with Scintrex CG-5 gravimeters. The observables involved in the model are instrumental reading, calibration functions of the gravimeters used and the absolute gravity values at the control stations. Gravity values at the gravity stations and local datum scale factors for each gravimeter were determined by least square method. The results indicate good adaptation of the tested model to network adjustments. The gravity value in the IFE-172 control station, located in Santa Maria, had the largest estimated correction of ?10.4 μGal (1 μGal = 10 nm/s2), and the largest residual for an observed reading was estimated in 0.043 reading unit. The largest correction to the calibration functions was estimated in 6.9 × 10-6mGal/reading unit.展开更多
A statistical correlation method is used to study the effect of instability of the calculation datum （ used in traditional method of indirect adjustment） on calculated gravity results, using data recorded by Longmen...A statistical correlation method is used to study the effect of instability of the calculation datum （ used in traditional method of indirect adjustment） on calculated gravity results, using data recorded by Longmen Mountain regional gravity network during 1996 -2007. The result shows that when this effect is corrected, anomalous gravity changes before the 2008 Wenchuan Ms8. 0 earthquake become obvious and characteristically distinctive. Thus the datum-stability problem must be considered when processing and analyzing data recorded by a regional gravity network.展开更多
In this paper, adjustment factors J and R put forward by professor Zhou Jiangwen are introduced and the nature of the adjustment factors and their role in evaluating adjustment structure is discussed and proved.
Efficient sensor node localization is a crucial part of many location-dependent applications that utilize wireless sensor networks (WSNs). To cope with the problem of insufficient bea-con node for localization,we desi...Efficient sensor node localization is a crucial part of many location-dependent applications that utilize wireless sensor networks (WSNs). To cope with the problem of insufficient bea-con node for localization,we design a beacon discovery protocol in this paper that helps the blind node to find beacons nearby and present an energy efficient scheme for the beacon that receives the request from a blind node to adjust its radio range. We obtain the relationship between the mean energy consumption with adjust-ment number by the mathematical analysis. Numerical results show that great energy saving is achieved when the optimal ad-justment number is adopted.展开更多
The prediction of solitary wave run-up has important practical significance in coastal and ocean engineering, but the calculation precision is limited in the existing models. For improving the calculation precision, a...The prediction of solitary wave run-up has important practical significance in coastal and ocean engineering, but the calculation precision is limited in the existing models. For improving the calculation precision, a solitary wave run-up calculation model was established based on artificial neural networks in this study. A back-propagation （BP） network with one hidden layer was adopted and modified with the additional momentum method and the auto-adjusting learning factor. The model was applied to calculation of solitary wave run-up. The correlation coefficients between the neural network model results and the experimental values was 0.996 5. By comparison with the correlation coefficient of 0.963 5, between the Synolakis formula calculation results and the experimental values, it is concluded that the neural network model is an effective method for calculation and analysis of solitary wave ran-up.展开更多
This paper considers the modeling and convergence of hyper-networked evolutionary games （HNEGs）. In an HNEG the network graph is a hypergraph, which allows the fundamental network game to be a multi-player one. Usin...This paper considers the modeling and convergence of hyper-networked evolutionary games （HNEGs）. In an HNEG the network graph is a hypergraph, which allows the fundamental network game to be a multi-player one. Using semi-tensor product of matrices and the fundamental evolutionary equation, the dynamics of an HNEG is obtained and we extend the results about the networked evolutionary games to show whether an HNEG is potential and how to calculate the potential. Then we propose a new strategy updating rule, called the cascading myopic best response adjustment rule （MBRAR）, and prove that under the cascading MBRAR the strategies of an HNEG will converge to a pure Nash equilibrium. An example is presented and discussed in detail to demonstrate the theoretical and numerical results.展开更多
文摘Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems.The aim of this paper is to provide a model based on neural networks(NNs)for multi-step-ahead traffic prediction.NNs'dependency on parameter setting is the major challenge in using them as a predictor.Given the fact that the best combination of NN parameters results in the minimum error of predicted output,the main problem is NN optimization.So,it is viable to set the best combination of the parameters according to a specific traffic behavior.On the other hand,an automatic method—which is applicable in general cases—is strongly desired to set appropriate parameters for neural networks.This paper defines a self-adjusted NN using the non-dominated sorting genetic algorithm II(NSGA-II)as a multi-objective optimizer for short-term prediction.NSGA-II is used to optimize the number of neurons in the first and second layers of the NN,learning ratio and slope of the activation function.This model addresses the challenge of optimizing a multi-output NN in a self-adjusted way.Performance of the developed network is evaluated by application to both univariate and multivariate traffic flow data from an urban highway.Results are analyzed based on the performance measures,showing that the genetic algorithm tunes the NN as well without any manually pre-adjustment.The achieved prediction accuracy is calculated with multiple measures such as the root mean square error(RMSE),and the RMSE value is 10 and 12 in the best configuration of the proposed model for single and multi-step-ahead traffic flow prediction,respectively.
基金Project (40174003) supported by the National Natural Science Foundation of China
文摘Study on solving nonlinear least squares adjustment by parameters is one of the most important and new subjects in modern surveying and mapping field . Many researchers have done a lot of work and gained some solving methods. These methods mainly include iterative algorithms and direct algorithms mainly. The former searches some methods of rapid convergence based on which surveying adjustment is a kind of problem of nonlinear programming. Among them the iterative algorithms of the most in common use are the Gauss-Newton method, damped least quares, quasi-Newton method and some mutations etc. Although these methods improved the quantity of the observation results to a certain degree, and increased the accuracy of the adjustment results, what we want is whether the initial values of unknown parameters are close to their real values. Of course, the model of the latter has better degree in linearity, that is to say, they nearly have the meaning of deeper theories researches. This paper puts forward a kind of method of solving the problems of nonlinear least squares adjustment by parameters based on neural network theory, and studies its stability and convergency. The results of calculating of living example indicate the method acts well for solving parameters problems by nonlinear least squares adjustment without giving exact approximation of parameters.
文摘A new gravity base network in the south of the Tibetan Plateau was established with a FG5X absolute gravimeter and three CG-6 gravimeters.The gravity base network consists of 10 absolute gravity points and 17 relative gravity points.Processing of the absolute data,pre-processing of the relative data and gravity network adjustment model are briefly described.Based a constrained weighted least squares,the combined adjustment of absolute and relative gravity measurements results in the gravity values with a precision of about±4.1μGal.
基金The study was supported by the National Science Fund of the Ministry of Education and Science(project DN05/14-2016,to JAD).
文摘Electroencephalographic studies using graph theoretic analysis have found aberrations in functional connectivity in children with developmental dyslexia.However,how the training with visual tasks can change the functional connectivity of the semantic network in developmental dyslexia is still unclear.We looked for differences in local and global topological properties of functional networks between 21 healthy controls and 22 dyslexic children(8–9 years old)before and after training with visual tasks in this prospective case-control study.The minimum spanning tree method was used to construct the subjects’brain networks in multiple electroencephalographic frequency ranges during a visual word/pseudoword discrimination task.We found group differences in the theta,alpha,beta and gamma bands for four graph measures suggesting a more integrated network topology in dyslexics before the training compared to controls.After training,the network topology of dyslexic children had become more segregated and similar to that of the controls.In theθ,αandβ1-frequency bands,compared to the controls,the pre-training dyslexics exhibited a reduced degree and betweenness centrality of the left anterior temporal and parietal regions.The simultaneous appearance in the left hemisphere of hubs in temporal and parietal(α,β1),temporal and superior frontal cortex(θ,α),parietal and occipitotemporal cortices(β1),identified in the networks of normally developing children was not present in the brain networks of dyslexics.After training,the hub distribution for dyslexics in the theta and beta1 bands had become similar to that of the controls.In summary,our findings point to a less efficient network configuration in dyslexics compared to a more optimal global organization in the controls.This is the first study to investigate the topological organization of functional brain networks of Bulgarian dyslexic children.Approval for the study was obtained from the Ethics Committee of the Institute of Neurobiology and the Institute for Population and Human Studies,Bulgarian Academy of Sciences(approval No.02-41/12.07.2019)on March 28,2017,and the State Logopedic Center and the Ministry of Education and Science(approval No.09-69/14.03.2017)on July 12,2019.
基金ItemSponsored by Provincial Natural Science Foundation of Hebei Province of China (E2004000206)
文摘In the strip rolling process, shape control system possesses the characteristics of nonlinearity, strong coupling, time delay and time variation. Based on self adapting Elman dynamic recursion network prediction model, the fuzzy control method was used to control the shape on four-high cold mill. The simulation results showed that the system can be applied to real time on line control of the shape.
文摘Focused on various BP algorithms with variable learning rate based on network system error gradient, a modified learning strategy for training non-linear network models is developed with both the incremental and the decremental factors of network learning rate being adjusted adaptively and dynamically. The golden section law is put forward to build a relationship between the network training parameters, and a series of data from an existing model is used to train and test the network parameters. By means of the evaluation of network performance in respect to convergent speed and predicting precision, the effectiveness of the proposed learning strategy can be illustrated.
文摘Gravity observations adjustment is studied having in view to take full advantage of the modern technology of gravity measurement. We present here results of a test performed with the mathematical model proposed by our group, on the adjustment of gravity observations carried out on network design. Additionally, considering the recent improvement on instrumental technology in gravimetry, that model was modified to take into account possible nonlinear local datum scale factors, in a 1900 mGal range network, and to check its significance for microgal precision measurements. The data set of the Brazilian Fundamental Gravity Network was used as case study. With about 1900 mGal gravity range and 11 control stations the Brazilian Fundamental Gravity Network (BFGN) was used as case study. It was established mainly with the use of LaCoste & Romberg, model G, gravimeters and new additional observations with Scintrex CG-5 gravimeters. The observables involved in the model are instrumental reading, calibration functions of the gravimeters used and the absolute gravity values at the control stations. Gravity values at the gravity stations and local datum scale factors for each gravimeter were determined by least square method. The results indicate good adaptation of the tested model to network adjustments. The gravity value in the IFE-172 control station, located in Santa Maria, had the largest estimated correction of ?10.4 μGal (1 μGal = 10 nm/s2), and the largest residual for an observed reading was estimated in 0.043 reading unit. The largest correction to the calibration functions was estimated in 6.9 × 10-6mGal/reading unit.
基金supported by the Eathquake Science Join Foundation( A07030)
文摘A statistical correlation method is used to study the effect of instability of the calculation datum （ used in traditional method of indirect adjustment） on calculated gravity results, using data recorded by Longmen Mountain regional gravity network during 1996 -2007. The result shows that when this effect is corrected, anomalous gravity changes before the 2008 Wenchuan Ms8. 0 earthquake become obvious and characteristically distinctive. Thus the datum-stability problem must be considered when processing and analyzing data recorded by a regional gravity network.
文摘In this paper, adjustment factors J and R put forward by professor Zhou Jiangwen are introduced and the nature of the adjustment factors and their role in evaluating adjustment structure is discussed and proved.
基金Supported by the National Natural Science Foundation of China(60633020)
文摘Efficient sensor node localization is a crucial part of many location-dependent applications that utilize wireless sensor networks (WSNs). To cope with the problem of insufficient bea-con node for localization,we design a beacon discovery protocol in this paper that helps the blind node to find beacons nearby and present an energy efficient scheme for the beacon that receives the request from a blind node to adjust its radio range. We obtain the relationship between the mean energy consumption with adjust-ment number by the mathematical analysis. Numerical results show that great energy saving is achieved when the optimal ad-justment number is adopted.
基金supported by State Key Development Program of Basic Research of China (Grant No.2010CB429001)
文摘The prediction of solitary wave run-up has important practical significance in coastal and ocean engineering, but the calculation precision is limited in the existing models. For improving the calculation precision, a solitary wave run-up calculation model was established based on artificial neural networks in this study. A back-propagation （BP） network with one hidden layer was adopted and modified with the additional momentum method and the auto-adjusting learning factor. The model was applied to calculation of solitary wave run-up. The correlation coefficients between the neural network model results and the experimental values was 0.996 5. By comparison with the correlation coefficient of 0.963 5, between the Synolakis formula calculation results and the experimental values, it is concluded that the neural network model is an effective method for calculation and analysis of solitary wave ran-up.
基金supported partly by National Natural Science Foundation of China(Nos.61074114 and 61273013)
文摘This paper considers the modeling and convergence of hyper-networked evolutionary games （HNEGs）. In an HNEG the network graph is a hypergraph, which allows the fundamental network game to be a multi-player one. Using semi-tensor product of matrices and the fundamental evolutionary equation, the dynamics of an HNEG is obtained and we extend the results about the networked evolutionary games to show whether an HNEG is potential and how to calculate the potential. Then we propose a new strategy updating rule, called the cascading myopic best response adjustment rule （MBRAR）, and prove that under the cascading MBRAR the strategies of an HNEG will converge to a pure Nash equilibrium. An example is presented and discussed in detail to demonstrate the theoretical and numerical results.
