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Energy Engineering 2024年第4期121卷 1007-1026页

作者： Zhuo Chen Ningning Wang Wenbo Jin Dui Li The Second Oil Production Plant Sinopec Northwest Oilfield CompanyUrumqi830011China Yingmaili Oil and Gas Production Management Area PetroChina Tarim Oilfield CompanyKorla841001China College of Petroleum Engineering Xi’an Shiyou UniversityXi’an710065China

A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines.To ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines.Aiming at the shortcomings of the ENN prediction model,which easily falls into the local minimum value and weak generalization ability in the implementation process,an optimized ENN prediction model based on the IRSA is proposed.The validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil pipelines.The two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%,respectively.Additionally,it shows a higher prediction accuracy compared to the ENN prediction model.In fact,the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process,which can overcome the shortcomings of the ENN prediction model,such as weak generalization ability and tendency to fall into the local minimum value,so that it has the advantages of strong implementation and high prediction accuracy.

关键词： Waxy crude oil wax deposition rate chaotic map improved reptile search algorithm elman neural network prediction accuracy

Critical Review on Improved Electrochemical Impedance Spectroscopy-cuckoo Search-elman neural network Modeling Methods for Whole-life-cycle Health State Estimation of Lithium-ion Battery Energy Storage Systems

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Protection and Control of Modern Power Systems 2024年第2期9卷 75-100页

作者： Ran Xiong Shunli Wang Paul Takyi-Aninakwa Siyu Jin Carlos Fernandez Qi Huang Weihao Hu Wei Zhan School of Information Engineering Southwest University of Science and TechnologyMianyangChina School of Information Engineering Southwest University of Science and TechnologyMianyang621010 China Mechanical Engineering from Kwame Nkrumah Uni-versity of Science and Technology Ghana Northeastern University ShenyangChina Robert Gordon University Scotland Southwest University of Science and Technology(SWUST) Mian-yangChina University of Electronic Science and Technology of China ChengduChina Xi’an Jiaotong University Xi’anChina SPIC Southwest Energy Research Institute ChengduChina

Efficient and accurate health state estimation is crucial for lithium-ion battery(LIB)performance monitoring and economic evaluation.Effectively estimating the health state of LIBs online is the key but is also the most difficult task for energy storage systems.With high adaptability and applicability advantages,battery health state estimation based on data-driven techniques has attracted extensive attention from researchers around the world.Artificial neural network(ANN)-based methods are often used for state estimations of LIBs.As one of the ANN methods,the elman neural network(ENN)model has been improved to estimate the battery state more efficiently and accurately.In this paper,an improved ENN estimation method based on electrochemical impedance spectroscopy(EIS)and cuckoo search(CS)is established as the EIS-CS-ENN model to estimate the health state of LIBs.Also,the paper conducts a critical review of various ANN models against the EIS-CS-ENN model.This demonstrates that the EIS-CS-ENN model outperforms other models.The review also proves that,under the same conditions,selecting appropriate health indicators(HIs)according to the mathematical modeling ability and state requirements are the keys in estimating the health state efficiently.In the calculation process,several evaluation indicators are adopted to analyze and compare the modeling accuracy with other existing methods.Through the analysis of the evaluation results and the selection of HIs,conclusions and suggestions are put forward.Also,the robustness of the EIS-CS-ENN model for the health state estimation of LIBs is verified.

关键词： Lithium-ion battery health state esti-mation elman neural network electrochemical imped-ance spectroscopy cuckoo search health indicators

Discrimination of neutrons and γ-rays in liquid scintillator based on elman neural network

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Chinese Physics C 2016年第8期40卷 130-135页

作者：张才勋林兴德赵建玲余训臻王力朱敬军幸浩洋 Key Laboratory of Radiation Physics and Technology of Ministry of Education Institute of Nuclear Science and TechnologySichuan University School of Physical Science and Technology Sichuan University Key Laboratory of Particle and Radiation Imaging(Ministry of Education) and Department of Engineering Physics Tsinghua University

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.

关键词： liquid scintillator n/γ discrimination elman neural network BP neural network

A prediction model of NH3 concentration for swine house in cold region based on Empirical Mode Decomposition and elman neural network

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Information Processing in Agriculture 2019年第2期6卷 297-305页

作者： Weizheng Shen Xiao Fu Runtao Wang Yanling Yin Yan Zhang Udaybeer Singh Bilegtsaikhan Lkhagva Jian Sun College of Electrical and Information Northeast Agricultural UniversityHarbin 150030China Key Laboratory of Pig Breeding Facilities Engineering Ministry of AgricultureHarbin 150030China College of Ling Nan Normal University Zhanjiang 524048China

In order to improve the accuracy and reliability of ammonia(NH3)concentration prediction,which can provides a support to the ventilation control strategy,so as to reduce the impact of NH3 on the health and productivity of swine,this paper proposed an NH3 concentration prediction method based on Empirical Mode Decomposition(EMD)and elman neural network modelling.The NH3 concentration and other four environmental parameters including temperature,humidity,carbon dioxide and light intensity were decomposed into several different time-scale intrinsic mode functions(IMFs).Then,the elman neural network prediction model was used to predict each IMF.The predicted NH3 was obtained by reconstructing all the IMFs by EMD.The results show that for the proposed method,the determination coefficient between the predicted and real measured value is 0.9856,the Mean Absolute Error is 0.7088 ppm,the Root Mean Square Error is 0.9096 ppm,and the Mean Absolute Percentage Error is 0.41%.Compared with the elman neural network,the proposed method has a good improvement in the accuracy,and provide effective parameters for the environmental monitoring of the swine house and the regulation of the NH3 concentration.

关键词： Cold region’swine house elman neural network Empirical Mode Decomposition NH3 concentration prediction Environmental monitoring

elman neural network with Modified Grey Wolf Optimizer for Enhanced Wind Speed Forecasting

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Circuits and Systems 2016年第10期7卷 2975-2995页

作者： M. Madhiarasan S. N. Deepa Department of Electrical and Electronics Engineering Anna University Regional Campus Coimbatore India

The scope of this paper is to forecast wind speed. Wind speed, temperature, wind direction, relative humidity, precipitation of water content and air pressure are the main factors make the wind speed forecasting as a complex problem and neural network performance is mainly influenced by proper hidden layer neuron units. This paper proposes new criteria for appropriate hidden layer neuron unit’s determination and attempts a novel hybrid method in order to achieve enhanced wind speed forecasting. This paper proposes the following two main innovative contributions 1) both either over fitting or under fitting issues are avoided by means of the proposed new criteria based hidden layer neuron unit’s estimation. 2) elman neural network is optimized through Modified Grey Wolf Optimizer (MGWO). The proposed hybrid method (elman-MGWO) performance, effectiveness is confirmed by means of the comparison between Grey Wolf Optimizer (GWO), Adaptive Gbest-guided Gravitational Search Algorithm (GGSA), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Cuckoo Search (CS), Particle Swarm Optimization (PSO), Evolution Strategy (ES), Genetic Algorithm (GA) algorithms, meanwhile proposed new criteria effectiveness and precise are verified comparison with other existing selection criteria. Three real-time wind data sets are utilized in order to analysis the performance of the proposed approach. Simulation results demonstrate that the proposed hybrid method (elman-MGWO) achieve the mean square error AVG ± STD of 4.1379e-11 ± 1.0567e-15, 6.3073e-11 ± 3.5708e-15 and 7.5840e-11 ± 1.1613e-14 respectively for evaluation on three real-time data sets. Hence, the proposed hybrid method is superior, precise, enhance wind speed forecasting than that of other existing methods and robust.

关键词： elman neural network Modified Grey Wolf Optimizer Hidden Layer Neuron Units Forecasting Wind Speed

elman neural network Based on Particle Swarm Optimization for Prediction of GPS Rapid Clock Bias

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Elman Neural Network Based on Particle Swarm Optimization fo...

Wind Speed Short-Term Prediction Based on Empirical Wavelet Transform, Recurrent neural network and Error Correction

第十三届中国卫星导航年会

作者： Yifeng Liang Jiangning Xu Miao Wu Naval Univ.of Engineering

To improve the accuracy of the satellite rapid clock bias, a modified elman neural network clock bias prediction method based on particle swarm optimization（PSO） algorithm is proposed. The elman recurrent neural network is introduced to predict the clock bias, its weights and thresholds are improved by PSO algorithm to improve the training speed and prediction accuracy. Then,the optimization method is applied to the rapid clock bias prediction, and the steps of using this method for the rapid clock bias prediction are given. Finally,the optimization method is compared with common quadratic polynomial model, gray model and ultra rapid clock bias product IGU-P. The results show that the PSO-elman model achieves high accuracy and stability for four different types of GPS satellite clock, and its prediction accuracy and stability improved by 85%, 74%, 89% and 71%, 53%, 28% compared with QPM,GM（1,1） and IGU-P products, respectively.

关键词： Satellite atomic clock Clock bias prediction elman neural network Particle Swarm optimization

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Journal of Shanghai Jiaotong university(Science) 2024年第2期29卷 297-308页

作者：朱昶胜朱丽娜 School of Computer and Communication Lanzhou University of TechnologyLanzhou 730050China

Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy.However,owing to the stochastic and intermittent of wind speed,predicting wind speed accurately is difficult.A new hybrid deep learning model based on empirical wavelet transform,recurrent neural network and error correction for short-term wind speed prediction is proposed in this paper.The empirical wavelet transformation is applied to decompose the original wind speed series.The long short term memory network and the elman neural network are adopted to predict low-frequency and high-frequency wind speed sub-layers respectively to balance the calculation efficiency and prediction accuracy.The error correction strategy based on deep long short term memory network is developed to modify the prediction errors.Four actual wind speed series are utilized to verify the effectiveness of the proposed model.The empirical results indicate that the method proposed in this paper has satisfactory performance in wind speed prediction.

关键词： wind speed prediction empirical wavelet transform deep long short term memory network elman neural network error correction strategy

Existence of Periodic Solutions for an Output Hidden Feedback elman neural network

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Journal of Software Engineering and Applications 2020年第12期13卷 348-363页

作者： Valéry Covachev Zlatinka Covacheva Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia Bulgaria

We first recall the sufficient conditions for the existence of a periodic output of a modified elman neural network with a periodic input found by using Mawhin’s continuation theorem of coincidence degree theory. Us... 详细信息

We first recall the sufficient conditions for the existence of a periodic output of a modified Elman neural network with a periodic input found by using Mawhin’s continuation theorem of coincidence degree theory. Using this result, we obtain sufficient conditions for the existence of a periodic output for an output hidden feedback Elman neural network with a periodic input. Examples illustrating these sufficient conditions are given.

关键词： elman neural network Periodic Input and Output Mawhin’s Continuation Theorem

Establishment of NH_3-N Prediction Model in Aquaculture Water Based on elman neural network

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Meteorological and Environmental Research 2015年第10期6卷 19-22页

作者： Wang Xiang He Jixiang She Lei Zhang Jing Fisheries Research Institute Anhui Academy of Agricultural Sciences

In the present study, elman artificial neural network model was developed to predict the change of NH3-N in aquaculture water. The in- dexes including feed ration, dissolved oxygen in water, water temperature, air temperature, water turbidity, rainfall were recorded and chosen as the input variables, while the NHz-N content in the corresponding pond was chosen as output variable. The above data were collected everyday from June to October in 2014 and were used to develop model in this test, and the data collected in November of 2014 were chosen to evaluate the developed model. The results showed that the changing trend of NH3-N in aquaculture water could be simulated well by the model, the predictive absolute error mean was 0.016 mg/L, and Nash-Sutcliffe efficiency coefficient was 0.74. The prediction model based on elman neural network had a strong ability to describe the nonlinear dynamic changes of NH3-N content in aquaculture water, and it showed the good adaptability and accu- racy in practical application.

关键词： Aquaculture water Water quality forecast elman neural network Nonlinear systems China

Prediction of Internet Traffic Based on elman neural network

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Prediction of Internet Traffic Based on Elman Neural Network