Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department t...Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures.In hence,it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning.This paper proposed a new hybrid model for traffic flow forecasting,which is composed of the variational mode decomposition(VMD)method,the group method of data handling(GMDH)neural network,bi-directional long and short term memory(BILSTM)network and ELMAN network,and is optimized by the imperialist competitive algorithm(ICA)method.To illustrate the performance of the proposed model,there are several comparative experiments between the proposed model and other models.The experiment results show that 1)BILSTM network,GMDH network and ELMAN network have better predictive performance than other single models;2)VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model.The effect of VMD method is better than that of EEMD method and FEEMD method.To conclude,the proposed model which is made up of the VMD method,the ICA method,the BILSTM network,the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.展开更多
Autonomous underwater vehicles(AUV) work in a complex marine environment. Its system reliability and autonomous fault diagnosis are particularly important and can provide the basis for underwater vehicles to take corr...Autonomous underwater vehicles(AUV) work in a complex marine environment. Its system reliability and autonomous fault diagnosis are particularly important and can provide the basis for underwater vehicles to take corresponding security policy in a failure. Aiming at the characteristics of the underwater vehicle which has uncertain system and modeling difficulty, an improved Elman neural network is introduced which is applied to the underwater vehicle motion modeling. Through designing self-feedback connection with fixed gain in the unit connection as well as increasing the feedback of the output layer node, improved Elman network has faster convergence speed and generalization ability. This method for high-order nonlinear system has stronger identification ability. Firstly, the residual is calculated by comparing the output of the underwater vehicle model(estimation in the motion state) with the actual measured values. Secondly, characteristics of the residual are analyzed on the basis of fault judging criteria. Finally, actuator fault diagnosis of the autonomous underwater vehicle is carried out. The results of the simulation experiment show that the method is effective.展开更多
The rapid growth of computational power demand from scientific,business,and Web applications has led to the emergence of cloud-oriented data centers.These centers use pay-as-you-go execution environments that scale tr...The rapid growth of computational power demand from scientific,business,and Web applications has led to the emergence of cloud-oriented data centers.These centers use pay-as-you-go execution environments that scale transparently to the user.Load prediction is a significant cost-optimal resource allocation and energy saving approach for a cloud computing environment.Traditional linear or nonlinear prediction models that forecast future load directly from historical information appear less effective.Load classification before prediction is necessary to improve prediction accuracy.In this paper,a novel approach is proposed to forecast the future load for cloud-oriented data centers.First,a hidden Markov model(HMM) based data clustering method is adopted to classify the cloud load.The Bayesian information criterion and Akaike information criterion are employed to automatically determine the optimal HMM model size and cluster numbers.Trained HMMs are then used to identify the most appropriate cluster that possesses the maximum likelihood for current load.With the data from this cluster,a genetic algorithm optimized Elman network is used to forecast future load.Experimental results show that our algorithm outperforms other approaches reported in previous works.展开更多
A new type of recurrent neural network is discussed, which provides the potential for modelling unknown nonlinear systems. The proposed network is a generalization of the network described by Elman, which has three la...A new type of recurrent neural network is discussed, which provides the potential for modelling unknown nonlinear systems. The proposed network is a generalization of the network described by Elman, which has three layers including the input layer, the hidden layer and the output layer. The input layer is composed of two different groups of neurons, the group of external input neurons and the group of the internal context neurons. Since arbitrary connections can be allowed from the hidden layer to the context layer, the modified Elman network has more memory space to represent dynamic systems than the Elman network. In addition, it is proved that the proposed network with appropriate neurons in the context layer can approximate the trajectory of a given dynamical system for any fixed finite length of time. The dynamic backpropagation algorithm is used to estimate the weights of both the feedforward and feedback connections. The methods have been successfully applied to the modelling of nonlinear plants.展开更多
In this work, a new neutron and γ （n/γ） discrimination method based on an Elman Neural Network （ENN） is proposed to improve the discrimination performance of liquid scintillator （LS） detectors. Neutron and γ ...In this work, a new neutron and γ （n/γ） discrimination method based on an Elman Neural Network （ENN） is proposed to improve the discrimination performance of liquid scintillator （LS） detectors. Neutron and γ data were acquired from an EJ-335 LS detector, which was exposed in a 241Am-9Be radiation field. Neutron and γ events were discriminated using two methods of artificial neural network including the ENN and a typical Back Propagation Neural Network （BPNN） as a control. The results show that the two methods have different n/γ discrimination performances. Compared to the BPNN, the ENN provides an improved of Figure of Merit （FOM） in n/γ discrimination. The FOM increases from 0.907 4- 0.034 to 0.953 4- 0.037 by using the new method of the ENN. The proposed n/γdiscrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection.展开更多
基金Project(61873283)supported by the National Natural Science Foundation of ChinaProject(KQ1707017)supported by the Changsha Science&Technology Project,ChinaProject(2019CX005)supported by the Innovation Driven Project of the Central South University,China。
文摘Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures.In hence,it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning.This paper proposed a new hybrid model for traffic flow forecasting,which is composed of the variational mode decomposition(VMD)method,the group method of data handling(GMDH)neural network,bi-directional long and short term memory(BILSTM)network and ELMAN network,and is optimized by the imperialist competitive algorithm(ICA)method.To illustrate the performance of the proposed model,there are several comparative experiments between the proposed model and other models.The experiment results show that 1)BILSTM network,GMDH network and ELMAN network have better predictive performance than other single models;2)VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model.The effect of VMD method is better than that of EEMD method and FEEMD method.To conclude,the proposed model which is made up of the VMD method,the ICA method,the BILSTM network,the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.
基金Project(2012T50331)supported by China Postdoctoral Science FoundationProject(2008AA092301-2)supported by the High-Tech Research and Development Program of China
文摘Autonomous underwater vehicles(AUV) work in a complex marine environment. Its system reliability and autonomous fault diagnosis are particularly important and can provide the basis for underwater vehicles to take corresponding security policy in a failure. Aiming at the characteristics of the underwater vehicle which has uncertain system and modeling difficulty, an improved Elman neural network is introduced which is applied to the underwater vehicle motion modeling. Through designing self-feedback connection with fixed gain in the unit connection as well as increasing the feedback of the output layer node, improved Elman network has faster convergence speed and generalization ability. This method for high-order nonlinear system has stronger identification ability. Firstly, the residual is calculated by comparing the output of the underwater vehicle model(estimation in the motion state) with the actual measured values. Secondly, characteristics of the residual are analyzed on the basis of fault judging criteria. Finally, actuator fault diagnosis of the autonomous underwater vehicle is carried out. The results of the simulation experiment show that the method is effective.
基金Project(No.71131002) supported by the National Natural Science Foundation of China
文摘The rapid growth of computational power demand from scientific,business,and Web applications has led to the emergence of cloud-oriented data centers.These centers use pay-as-you-go execution environments that scale transparently to the user.Load prediction is a significant cost-optimal resource allocation and energy saving approach for a cloud computing environment.Traditional linear or nonlinear prediction models that forecast future load directly from historical information appear less effective.Load classification before prediction is necessary to improve prediction accuracy.In this paper,a novel approach is proposed to forecast the future load for cloud-oriented data centers.First,a hidden Markov model(HMM) based data clustering method is adopted to classify the cloud load.The Bayesian information criterion and Akaike information criterion are employed to automatically determine the optimal HMM model size and cluster numbers.Trained HMMs are then used to identify the most appropriate cluster that possesses the maximum likelihood for current load.With the data from this cluster,a genetic algorithm optimized Elman network is used to forecast future load.Experimental results show that our algorithm outperforms other approaches reported in previous works.
文摘A new type of recurrent neural network is discussed, which provides the potential for modelling unknown nonlinear systems. The proposed network is a generalization of the network described by Elman, which has three layers including the input layer, the hidden layer and the output layer. The input layer is composed of two different groups of neurons, the group of external input neurons and the group of the internal context neurons. Since arbitrary connections can be allowed from the hidden layer to the context layer, the modified Elman network has more memory space to represent dynamic systems than the Elman network. In addition, it is proved that the proposed network with appropriate neurons in the context layer can approximate the trajectory of a given dynamical system for any fixed finite length of time. The dynamic backpropagation algorithm is used to estimate the weights of both the feedforward and feedback connections. The methods have been successfully applied to the modelling of nonlinear plants.
基金Supported by National Natural Science Foundation of China(11275134,11475117)
文摘In this work, a new neutron and γ （n/γ） discrimination method based on an Elman Neural Network （ENN） is proposed to improve the discrimination performance of liquid scintillator （LS） detectors. Neutron and γ data were acquired from an EJ-335 LS detector, which was exposed in a 241Am-9Be radiation field. Neutron and γ events were discriminated using two methods of artificial neural network including the ENN and a typical Back Propagation Neural Network （BPNN） as a control. The results show that the two methods have different n/γ discrimination performances. Compared to the BPNN, the ENN provides an improved of Figure of Merit （FOM） in n/γ discrimination. The FOM increases from 0.907 4- 0.034 to 0.953 4- 0.037 by using the new method of the ENN. The proposed n/γdiscrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection.
