Prediction Model of Wax Deposition Rate in Waxy Crude Oil Pipelines by Elman Neural Network Based on Improved Reptile Search Algorithm

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.

ELMAN Neural Network with Modified Grey Wolf Optimizer for Enhanced Wind Speed Forecasting

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.

Existence of Periodic Solutions for an Output Hidden Feedback Elman Neural Network

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.

Actuator fault diagnosis of autonomous underwater vehicle based on improved Elman neural network

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.

Predication of plasma concentration of remifentanil based on Elman neural network

Due to the nature of ultra-short-acting opioid remifentanil of high time-varying,complex compartment model and low-accuracy of plasma concentration prediction,the traditional estimation method of population pharmacokinetics parameters,nonlinear mixed effects model(NONMEM),has the abuses of tedious work and plenty of man-made jamming factors.The Elman feedback neural network was built.The relationships between the patients’plasma concentration of remifentanil and time,patient’age,gender,lean body mass,height,body surface area,sampling time,total dose,and injection rate through network training were obtained to predict the plasma concentration of remifentanil,and after that,it was compared with the results of NONMEM algorithm.In conclusion,the average error of Elman network is 6.34%,while that of NONMEM is 18.99%.The absolute average error of Elman network is 27.07%,while that of NONMEM is 38.09%.The experimental results indicate that Elman neural network could predict the plasma concentration of remifentanil rapidly and stably,with high accuracy and low error.For the characteristics of simple principle and fast computing speed,this method is suitable to data analysis of short-acting anesthesia drug population pharmacokinetic and pharmacodynamics.

Establishment of NH_3-N Prediction Model in Aquaculture Water Based on ELMAN Neural Network

In the present study,ELMAN artificial neural network model was developed to predict the change of NH3-N in aquaculture water. The indexes including feed ration,dissolved oxygen in water,water temperature,air temperature,water turbidity,rainfall were recorded and chosen as the input variables,while the NH3-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 accuracy in practical application.

Study on the Elman Neural Network Operation Control Strategy of the Central Air Conditioning Chilled Water System

The stable operation of the central air conditioning water system always is a major difficulty for the control profession. Paper focus on the water system with multi variable, strong coupling, nonlinear, large time delay characteristics, presented use feed forward coupling compensation method, to eliminate the coupling effect between temperature and pressure. In this paper, the Elman neural network controller is designed for the first time, and the simulation results show that the response time of Elman neural network controller is shorter, the system is more stable and the overshoot is small.

The Research on the Methods of Diagnosing the Steam Turbine Based on the Elman Neural Network

This paper introduces a kind of diagnosis principle and learning algorithm of steam turbine fault diagnosis which based on Elman neural network. Comparing the results of the Elman neural network and the traditional BP neural network diagnosis, the results shows that Elman neural network is an effective way to improve the learning speed , effectively suppress the minimum defects that the traditional neural network easily trapped in, and shorten the autonomous learning time. All these proves that the Elman neural network is an effective way to diagnose the steam turbine.

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

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.

Study on Ecological Change Remote Sensing Monitoring Method Based on Elman Dynamic Recurrent Neural Network

In this paper, Hailin City of Heilongjiang Province, China is taken as the research area. As an important city in Heilongjiang Province, China, the sustainable development of its ecological environment is related to the opening up, economic prosperity and social stability of Northeast China. In this paper, the remote sensing ecological index (RSEI) of Hailin City in recent 20 years was calculated by using Landsat 5/8/9 series satellite images, and the temporal and spatial changes of the ecological environment in Hailin City were further analyzed and the influencing factors were discussed. From 2003 to 2023, the mean value of RSEI in Hailin City decreased and increased, and the ecological environment decreased slightly as a whole. RSEI declined most significantly from 2003 to 2008, and it increased from 2008 to 2013, decreased from 2013 to 2018, and increased from 2018 to 2023 again, with higher RSEI value in the south and lower RSEI value in the northwest. It is suggested to appropriately increase vegetation coverage in the northwest to improve ecological quality. As a result, the predicted value of Elman dynamic recurrent neural network model is consistent with the change trend of the mean value, and the prediction error converges quickly, which can accurately predict the ecological environment quality in the future study area.

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

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.

基于IWOA-SA-Elman神经网络的短期风电功率预测

由于风力发电的随机性和不确定性使其短期功率的预测工作十分困难,而神经网络模型依靠其强大的自学习能力在风电功率预测领域有着广泛的应用。但神经网络预测精度受初始权重影响较大,且易出现过拟合的问题。为此构建一种基于改进鲸鱼算法和模拟退火组合优化的Elman神经网络短期风电功率预测模型,模型首先利用改进鲸鱼算法结合模拟退火策略获得高质量神经网络初始权值,接着引入正则化损失函数防止其过拟合,最后以西班牙瓦伦西亚某风电场陆上短期风电功率为研究对象,将该算法与BP、LSTM、Elman、WOA-Elman、IWOA-Elman 5种神经网络算法进行算法性能测试对比,结果表明IWOA-SA-Elman神经网络模型预测误差最小,验证了该算法的合理性和有效性。

基于PSO-Elman神经网络的井底风温预测模型

目前井下风温预测大多采用BP神经网络,但其预测精度受学习样本数量的影响,且容易陷入局部最优,Elman神经网络具备局部记忆能力,提高了网络的稳定性和动态适应能力,但仍然存在收敛速度过慢、易陷入局部最优的问题。针对上述问题,采用粒子群优化(PSO)算法对Elman神经网络的权值和阈值进行优化,建立了基于PSO-Elman神经网络的井底风温预测模型。分析得出入风相对湿度、入风温度、地面大气压力和井筒深度是井底风温的主要影响因素,因此将其作为模型的输入数据,模型的输出数据为井底风温。在相同样本数据集下的实验结果表明:Elman模型迭代90次后收敛,PSO-Elman模型迭代41次后收敛,说明PSO-Elman模型收敛速度更快;与BP神经网络模型、支持向量回归模型和Elman模型相比,PSO-Elman模型的预测误差较低,平均绝对误差、均方误差(MSE)、平均绝对百分比误差分别为0.376 0℃,0.278 3,1.95%,决定系数R^(2)为0.992 4,非常接近1,表明预测模型具有良好的预测效果。实例验证结果表明,PSO-Elman模型的相对误差范围为-4.69%~1.27%,绝对误差范围为-1.06~0.29℃,MSE为0.26,整体预测精度可满足井下实际需要。

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

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 andγ data were acquired from an EJ-335 LS detector,which was exposed in a ^(241)Am-~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/γdiscrimination 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 ± 0.034 to 0.953 ± 0.037 by using the new method of the ENN.The proposed n/γ discrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection.

基于改进Elman神经网络的CFRP补强钢板界面脱粘预测研究

针对碳纤维复合材料(carbon fiber reinforced polymer, CFRP)补强钢结构出现内部界面脱粘损伤后难以观测的问题,结合Lamb波检测方法和神经网络提出了一种界面脱粘预测方法。搭建了基于Lamb波的CFRP补强钢板信号分析试验平台,利用ABAQUS软件建立了CFRP补强钢板的机电耦合有限元模型,并通过试验验证了有限元模型的准确性。将长方形和圆形两种脱粘形状的信号在时域和频域内进行分析,基于自适应遗传算法改进的Elman神经网络建立了CFRP补强钢板脱粘预测模型,并将与脱粘面积相关性较高的信号特征数据作为预测模型的特征数据。对预测模型进行性能测试,脱粘形状为长方形和圆形预测值的平均绝对百分比误差分别为3.03%和8.06%,结果表明改进的Elman网络对于脱粘损伤具有较好的预测精度。

Real-Time Fault Diagnosis for Gas Turbine Blade Based on Output-Hidden Feedback Elman Neural Network

In order to remotely monitor and maintain large-scale complex equipment in real time, China Telecom plans to create a total solution that integrates remote data collection, transmission, storage, analysis and prediction. This solution can provide manufacturers with proactive, systematic, integrated operation and maintenance service, and the data analysis and health forecasting are the most important part. This paper conducts health management for the turbine blades. Elman neural network, and improved Elman neural network, i.e., outputhidden feedback(OHF) Elman neural network are studied as the main research methods. The results verify the applicability of OHF Elman neural network.

FOUR-PARAMETER AUTOMATIC TRANSMISSION TECHNOLOGY FOR CONSTRUCTION VEHICLE BASED ON ELMAN RECURSIVE NEURAL NETWORK

From the viewpoint of energy saving and improving transmission efficiency,the ZL50E wheel loader is taken as the study object. And the system model is analyzed based on the transmission system of the construction vehicle. A new four-parameter shift schedule is presented,which can keep the torque converter working in the high efficiency area. The control algorithm based on the Elman recursive neural network is applied,and four-parameter control system is developed which is based on industrial computer. The system is used to collect data accurately and control 4D180 power-shift gearbox of ZL50E wheel loader shift timely. An experiment is done on automatic transmission test-bed,and the result indicates that the control system could reliably and safely work and improve the efficiency of hydraulic torque converter. Four-parameter shift strategy that takes into account the power consuming of the working pump has important operating significance and reflects the actual working status of construction vehicle.

Multicomponent Kinetic Determination by Wavelet Packet Transform Based Elman Recurrent Neural Network Method

This paper covers a novel method named wavelet packet transform based Elman recurrent neural network(WPTERNN) for the simultaneous kinetic determination of periodate and iodate. The wavelet packet representations of signals provide a local time-frequency description, thus in the wavelet packet domain, the quality of the noise removal can be improved. The Elman recurrent network was applied to non-linear multivariate calibration. In this case, by means of optimization, the wavelet function, decomposition level and number of hidden nodes for WPTERNN method were selected as D4, 5 and 5 respectively. A program PWPTERNN was designed to perform multicomponent kinetic determination. The relative standard error of prediction(RSEP) for all the components with WPTERNN, Elman RNN and PLS were 3.23%, 11.8% and 10.9% respectively. The experimental results show that the method is better than the others.

基于改进WOA-Elman模型的舆情热度预测研究

针对公共卫生舆情事件的突发性和破坏性等问题,为了更精准预测舆情发生时的热度走向,本文构建了基于改进鲸鱼算法(WOA)优化Elman神经网络的舆情热度预测模型。首先根据百度指数和360趋势,对2020年1月1日至同年2月19日时段“COVID-19”事件的时间序列指标进行选取;其次利用WOA优化Elman神经网络初始值和阈值的方法进行训练和预测;最后与标准BP神经网络模型、标准Elman神经网络模型进行对比分析。结果表明,改进WOA-Elman的平均绝对百分比误差、均方根误差分别为4.7843和219363.7844,该预测模型的预测结果与原始数据更吻合,预测精度和预测误差上更具优势,在解决突发公共卫生舆情事件热度预测问题上切实有效。

基于VMD-ELMAN的肌电信号对下肢关节角度预测

表面肌电信号(Surface Electromyography,sEMG)提前于人体动作产生,常用于预测人体行为运动意图。但由于其自身的非平稳性与时变特性,因此难以较为准确地预测人体下肢关节角度变化。文中研究人体下肢肌肉针对正常行走、上下楼梯这3种动作进行的肌肉选取,提出了一种VMD-ELMAN角度拟合算法,提高了表面肌电信号角度预测精度,增强了角度预测的实时性,为提升人与外骨骼设备人机融合度提供了有效的解决方案。实验结果表明,相比常见角度拟合算法,所提算法的时间耗时较短,在3种常见动作中,髋关节角度预测值RMSE的最高精度达0.5789,膝关节角度预测值RMSE均在0.2以内,预测精度均优于常见模型,模型鲁棒性强。