The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial...The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.展开更多
Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in hi...Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in highenergy particle and nuclear physics experiments.The inherent properties of the detector and hardware imply that particles with relatively high energies probably often generate saturated signals.Usually,these saturated signals are discarded during data processing,and therefore,some useful information is lost.Thus,it is worth restoring the saturated signals to their normal form.The mapping from a saturated signal waveform to a normal signal waveform constitutes a regression problem.Given that the scintillator and collection usually do not form a linear system,typical regression methods such as multi-parameter fitting are not immediately applicable.One important advantage of ANNs is their capability to process nonlinear regression problems.To recover the saturated signal,three typical ANNs were tested including backpropagation(BP),simple recurrent(Elman),and generalized radial basis function(GRBF)neural networks(NNs).They represent a basic network structure,a network structure with feedback,and a network structure with a kernel function,respectively.The saturated waveforms were produced mainly by the environmental gamma in a liquid scintillation detector for the China Dark Matter Detection Experiment(CDEX).The training and test data sets consisted of 6000 and 3000 recordings of background radiation,respectively,in which saturation was simulated by truncating each waveform at 40%of the maximum signal.The results show that the GBRF-NN performed best as measured using a Chi-squared test to compare the original and reconstructed signals in the region in which saturation was simulated.A comparison of the original and reconstructed signals in this region shows that the GBRF neural network produced the best performance.This ANN demonstrates a powerful efficacy in terms of solving the saturation recovery problem.The proposed method outlines new ideas and possibilities for the recovery of saturated signals in high-energy particle and nuclear physics experiments.This study also illustrates an innovative application of machine learning in the analysis of experimental data in particle physics.展开更多
An effective approach for describing complicated water quality processes is very important for river water quality management. We built two artificial neural network(ANN) models,a feed-forward back-propagation(BP) mod...An effective approach for describing complicated water quality processes is very important for river water quality management. We built two artificial neural network(ANN) models,a feed-forward back-propagation(BP) model and a radial basis function(RBF) model,to simulate the water quality of the Yangtze and Jialing Rivers in reaches crossing the city of Chongqing,P. R. China. Our models used the historical monitoring data of biological oxygen demand,dissolved oxygen,ammonia,oil and volatile phenolic compounds. Comparison with the one-dimensional traditional water quality model suggest that both BP and RBF models are superior; their higher accuracy and better goodness-of-fit indicate that the ANN calculation of water quality agrees better with measurement. It is demonstrated that ANN modeling can be a tool for estimating the water quality of the Yangtze River. Of the two ANN models,the RBF model calculates with a smaller mean error,but a larger root mean square error. More effort to identify out the causes of these differences would help optimize the structures of neural network water-quality models.展开更多
Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a n...Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.展开更多
This paper studies the generalization capability of feedforward neural networks (FNN).The mechanism of FNNs for classification is investigated from the geometric and probabilistic viewpoints. It is pointed out that th...This paper studies the generalization capability of feedforward neural networks (FNN).The mechanism of FNNs for classification is investigated from the geometric and probabilistic viewpoints. It is pointed out that the outputs of the output layer in the FNNs for classification correspond to the estimates of posteriori probability of the input pattern samples with desired outputs 1 or 0. The theorem for the generalized kernel function in the radial basis function networks (RBFN) is given. For an 2-layer perceptron network (2-LPN). an idea of using extended samples to improve generalization capability is proposed. Finally. the experimental results of radar target classification are given to verify the generaliztion capability of the RBFNs.展开更多
The roll motions of ships advancing in heavy seas have severe impacts on the safety of crews,vessels,and cargoes;thus,it must be damped.This study presents the design of a rudder roll damping autopilot by utilizing th...The roll motions of ships advancing in heavy seas have severe impacts on the safety of crews,vessels,and cargoes;thus,it must be damped.This study presents the design of a rudder roll damping autopilot by utilizing the dual extended Kalman filter(DEKF)trained radial basis function neural networks(RBFNN)for the surface vessels.The autopilot system constitutes the roll reduction controller and the yaw motion controller implemented in parallel.After analyzing the advantages of the DEKF-trained RBFNN control method theoretically,the ship’s nonlinear model with environmental disturbances was employed to verify the performance of the proposed stabilization system.Different sailing scenarios were conducted to investigate the motion responses of the ship in waves.The results demonstrate that the DEKF RBFNN based control system is efficient and practical in reducing roll motions and following the path for the ship sailing in waves only through rudder actions.展开更多
为应对当今供应链库存管理面临的牛鞭效应、两时间尺度特性和不确定性干扰等挑战,开发了一种基于径向基函数神经网络(radial basis function neural network,RBFNN)的两时间尺度供应链H_(∞)最优控制器。利用奇异摄动理论将原两时间尺...为应对当今供应链库存管理面临的牛鞭效应、两时间尺度特性和不确定性干扰等挑战,开发了一种基于径向基函数神经网络(radial basis function neural network,RBFNN)的两时间尺度供应链H_(∞)最优控制器。利用奇异摄动理论将原两时间尺度供应链模型分解为2个具有不同时间尺度的独立子系统;创新性地使用RBFNN在线近似补偿子系统的不确定项,进而采用H_(∞)控制来抑制RBFNN近似误差带来的不确定性。在理论层面上分析证明了所提方法的稳定性。通过一个电视机生产流程仿真案例,验证了所提方法相比2种其他两时间尺度问题解决方法,具有更高的跟踪控制精度和应用可行性。展开更多
基金Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110303)the Beijing Municipal Science & Technology Project,China (Grant No. Z111100064311001)
文摘The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.
基金supported by the ‘‘Detection of very low-flux background neutrons in China Jinping Underground Laboratory’’ project of the National Natural Science Foundation of China(No.11275134)
文摘Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in highenergy particle and nuclear physics experiments.The inherent properties of the detector and hardware imply that particles with relatively high energies probably often generate saturated signals.Usually,these saturated signals are discarded during data processing,and therefore,some useful information is lost.Thus,it is worth restoring the saturated signals to their normal form.The mapping from a saturated signal waveform to a normal signal waveform constitutes a regression problem.Given that the scintillator and collection usually do not form a linear system,typical regression methods such as multi-parameter fitting are not immediately applicable.One important advantage of ANNs is their capability to process nonlinear regression problems.To recover the saturated signal,three typical ANNs were tested including backpropagation(BP),simple recurrent(Elman),and generalized radial basis function(GRBF)neural networks(NNs).They represent a basic network structure,a network structure with feedback,and a network structure with a kernel function,respectively.The saturated waveforms were produced mainly by the environmental gamma in a liquid scintillation detector for the China Dark Matter Detection Experiment(CDEX).The training and test data sets consisted of 6000 and 3000 recordings of background radiation,respectively,in which saturation was simulated by truncating each waveform at 40%of the maximum signal.The results show that the GBRF-NN performed best as measured using a Chi-squared test to compare the original and reconstructed signals in the region in which saturation was simulated.A comparison of the original and reconstructed signals in this region shows that the GBRF neural network produced the best performance.This ANN demonstrates a powerful efficacy in terms of solving the saturation recovery problem.The proposed method outlines new ideas and possibilities for the recovery of saturated signals in high-energy particle and nuclear physics experiments.This study also illustrates an innovative application of machine learning in the analysis of experimental data in particle physics.
基金Funded by the Natural Science Foundation of China (No. 59778021)
文摘An effective approach for describing complicated water quality processes is very important for river water quality management. We built two artificial neural network(ANN) models,a feed-forward back-propagation(BP) model and a radial basis function(RBF) model,to simulate the water quality of the Yangtze and Jialing Rivers in reaches crossing the city of Chongqing,P. R. China. Our models used the historical monitoring data of biological oxygen demand,dissolved oxygen,ammonia,oil and volatile phenolic compounds. Comparison with the one-dimensional traditional water quality model suggest that both BP and RBF models are superior; their higher accuracy and better goodness-of-fit indicate that the ANN calculation of water quality agrees better with measurement. It is demonstrated that ANN modeling can be a tool for estimating the water quality of the Yangtze River. Of the two ANN models,the RBF model calculates with a smaller mean error,but a larger root mean square error. More effort to identify out the causes of these differences would help optimize the structures of neural network water-quality models.
文摘Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.
文摘This paper studies the generalization capability of feedforward neural networks (FNN).The mechanism of FNNs for classification is investigated from the geometric and probabilistic viewpoints. It is pointed out that the outputs of the output layer in the FNNs for classification correspond to the estimates of posteriori probability of the input pattern samples with desired outputs 1 or 0. The theorem for the generalized kernel function in the radial basis function networks (RBFN) is given. For an 2-layer perceptron network (2-LPN). an idea of using extended samples to improve generalization capability is proposed. Finally. the experimental results of radar target classification are given to verify the generaliztion capability of the RBFNs.
基金a part of the project titled ’Intelligent Control for Surface Vessels Based on Kalman Filter Variants Trained Radial Basis Function Neural Networks’ partially funded by the Institutional Grants Scheme(TGRS 060515)of Tasmania,Australia
文摘The roll motions of ships advancing in heavy seas have severe impacts on the safety of crews,vessels,and cargoes;thus,it must be damped.This study presents the design of a rudder roll damping autopilot by utilizing the dual extended Kalman filter(DEKF)trained radial basis function neural networks(RBFNN)for the surface vessels.The autopilot system constitutes the roll reduction controller and the yaw motion controller implemented in parallel.After analyzing the advantages of the DEKF-trained RBFNN control method theoretically,the ship’s nonlinear model with environmental disturbances was employed to verify the performance of the proposed stabilization system.Different sailing scenarios were conducted to investigate the motion responses of the ship in waves.The results demonstrate that the DEKF RBFNN based control system is efficient and practical in reducing roll motions and following the path for the ship sailing in waves only through rudder actions.
文摘为应对当今供应链库存管理面临的牛鞭效应、两时间尺度特性和不确定性干扰等挑战,开发了一种基于径向基函数神经网络(radial basis function neural network,RBFNN)的两时间尺度供应链H_(∞)最优控制器。利用奇异摄动理论将原两时间尺度供应链模型分解为2个具有不同时间尺度的独立子系统;创新性地使用RBFNN在线近似补偿子系统的不确定项,进而采用H_(∞)控制来抑制RBFNN近似误差带来的不确定性。在理论层面上分析证明了所提方法的稳定性。通过一个电视机生产流程仿真案例,验证了所提方法相比2种其他两时间尺度问题解决方法,具有更高的跟踪控制精度和应用可行性。
