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Journal articles on the topic 'Elman'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Wang, Xiu Fang, Chong Chong Liang, Jian Guo Jiang, and Li Li Ju. "Sensor Compensation Based on Adaptive Ant Colony Neural Networks." Advanced Materials Research 301-303 (July 2011): 876–80. http://dx.doi.org/10.4028/www.scientific.net/amr.301-303.876.

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In order to improve work stability and measurement accuracy of drilling inclinometer, and overcome the poor stability of Elman networks and lower compensation precision of genetic Elman neural networks, we combined ant colony algorithm and neural networks, using the Adaptive Ant Colony Algorithm that its pheromone evaporation factorand pheromone update strategy adjust adaptively to optimize Elman neural network weights and thresholds, and applied it to drilling inclinometer sensor compensation. Simulation results show that the compensation effect of adaptive ant colony Elman neural networks is better than that of Elman networks and genetic Elman networks, the compensation accuracy is 10-8.

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2

Ji, Zhi Qiang, Ming Wei, Qi Meng Wu, and Xiao Le Wu. "Simulation of EMP Inject Effects Based on Improved Elman Network." Advanced Materials Research 986-987 (July 2014): 2019–22. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.2019.

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In order to quickly determine the performance of a transient voltage suppressor (TVS), improve time domain identification capability of Elman network, the simulation of electromagnetic pulse (EMP) inject effects based on improved Elman network is proposed. Derivation proved that improved Elman network trained by standard BP algorithm has a similar form with the basic Elman network trained dynamic BP algorithm. We establish and improve its Elman network predictive modeling based on the measured parameters of TVS and then demonstrate that improved Elman network has the characteristics of quick speed, high precision, good performance and strong generalization ability, and broad use of prospects.

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3

You, Wen Xia, Jun Xiao Chang, Zi Heng Zhou, and Ji Lu. "Short-Term Load Forecasting Based on GA-Elman Model." Advanced Materials Research 986-987 (July 2014): 520–23. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.520.

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Elman Neural Network is a typical neural-network which shares the characteristics of multiple-layer and dynamic recurrent, and it’s more suitable than BP Neural Network when it’s applied to forecast the short-term load with periodicity and similarity. To solve the problem that Elman Neural Network lacks learning efficiency, GA-Elman model is established by optimizing the weights and thresholds using Genetic Algorithm. An example is then given to prove the effectiveness of GA-Elman model, using the load data of a certain region. Relative error and MSE have been considered as criterions to analyze the results of load forecasting. By comparing the results calculated by BP, Elman and GA-Elman model, the effectiveness of GA-Elman model is verified, which will improve the accuracy of short-term load forecasting.

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4

Wu, Hai Chao, and Chong Zhi Song. "Engine Gearbox Fault Diagnosis Using Modified Elman Neural Network and ACO Algorithm." Applied Mechanics and Materials 190-191 (July 2012): 982–86. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.982.

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Analyzed the shortcomings of Elman network, the paper put forward a modified Elman network, combined Ant Colony Optimization (ACO) algorithm to train the modified Elman network, and implement trained NN (Neural Network) to fault diagnosis of engine gearbox. Using conventional ’frequency domain’ analysis method, modified Elman network fault diagnosis of the gearbox was carried out. The results proved that fault diagnosis of engine gearbox based on the modified Elman neural network and ACO has better precision and diagnoses gearbox effectively, which imp roves the effectiveness and quality of the diagnosis.

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5

Huang, Renquan, and Jing Tian. "Wavelet-Based Elman Neural Network with the Modified Differential Evolution Algorithm for Forecasting Foreign Exchange Rates." Journal of Systems Science and Information 9, no. 4 (August 1, 2021): 421–39. http://dx.doi.org/10.21078/jssi-2021-421-19.

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Abstract It is challenging to forecast foreign exchange rates due to the non-linear characters of the data. This paper applied a wavelet-based Elman neural network with the modified differential evolution algorithm to forecast foreign exchange rates. Elman neural network has dynamic characters because of the context layer in the structure. It makes Elman neural network suit for time series problems. The main factors, which affect the accuracy of the Elman neural network, included the transfer functions of the hidden layer and the parameters of the neural network. We applied the wavelet function to replace the sigmoid function in the hidden layer of the Elman neural network, and we found there was a “disruption problem” caused by the non-linear performance of the wavelet function. It didn’t improve the performance of the Elman neural network, but made it get worse in reverse. Then, the modified differential evolution algorithm was applied to train the parameters of the Elman neural network. To improve the optimizing performance of the differential evolution algorithm, the crossover probability and crossover factor were modified with adaptive strategies, and the local enhanced operator was added to the algorithm. According to the experiment, the modified algorithm improved the performance of the Elman neural network, and it solved the “disruption problem” of applying the wavelet function. These results show that the performance of the Elman neural network would be improved if both of the wavelet function and the modified differential evolution algorithm were applied integratedly.

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6

Wei, Lin, Yongqing Wu, Hua Fu, and Yuping Yin. "Modeling and Simulation of Gas Emission Based on Recursive Modified Elman Neural Network." Mathematical Problems in Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/9013839.

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For the purpose of achieving more effective prediction of the absolute gas emission quantity, this paper puts forward a new model based on the hidden recurrent feedback Elman. The recursive part of classic Elman cannot be adjusted because it is fixed. To a certain extent, this drawback affects the approximation ability of the Elman, so this paper adds the correction factors in recursive part and uses the error feedback to determine the parameters. The stability of the recursive modified Elman neural network is proved in the sense of Lyapunov stability theory, and the optimal learning rate is given. With the historical data of mine actual monitoring to experiment and analysis, the results show that the recursive modified Elman neural network model can effectively predict the gas emission and improve the accuracy and efficiency of prediction compared with the classic Elman prediction model.

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7

Zhao, Xian Jia, Ling Yun Wen, and Han Yu Cai. "Research of Generated Power Forecasting Model Based on the Fusion of Elman NN and ACOA for Photovoltaic System." Applied Mechanics and Materials 392 (September 2013): 628–31. http://dx.doi.org/10.4028/www.scientific.net/amm.392.628.

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A new generated power forecasting model based on the fusion of Elman neural networks (Elman NN) and ant colony optimization algorithm (ACOA) for photovoltaic system are presented in this paper. Elman NN owns stronger dynamic performance and calculation ability. And it can characterize complicated dynamics behavior. ACOA was used to optimize to improve the generalization performance of Elman NN model. The testing results show that new approaches can improve effectively the precision of generated power forecasting.

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8

Ding, Shan, Yixin Yin, Wei Huang, Jie Dong, and Xue Ming Ma. "Temperature Identification of Electrical Heating Furnace Based on Elman Network Combined with Improved PSO." Applied Mechanics and Materials 20-23 (January 2010): 82–87. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.82.

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A temperature model of the electrical heating furnace which is commonly used in industry is built by means of Elman neural network combined with an improved particle swarm optimization (IPSO). The input is duty cycle, and IPSO is used to optimize the weights and threshold values of Elman neural network to improve convergence capability and generalization performance of Elman network. Results show that the method performs better than ordinary Elman network in convergence speed and accuracy.

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9

Zhang, Zhisheng, and Wenjie Gong. "Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks." Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/7910971.

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Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means ofK-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

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10

Bu, Yu Hong. "Research in Elman Neural Network for AFR Model of Automotive Engine." Advanced Materials Research 204-210 (February 2011): 755–59. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.755.

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Air fuel ratio is a key index affecting power performance and fuel economy and exhaust emissions of the gasoline engine, whose accurate model is the foundation of accuracy air fuel ratio control. In the paper, at first, it has studied the Elman neural network (NN) simulation model of Air Fuel ratio physical model of automotive engine. Second, employing the SI-V8 in en-DYNA engine model as experimental device, the paper discussed the structure determination of Elman neural network; finally, it compared model identification performance between Elman and BP neural network. Experiment results show the generalization performance of neural network does not have a linear relationship to the neurons in hidden layer of Elman NN, and the air fuel ratio based on Elman neural network is better than the air fuel ratio model based on BP neural network. The average relative error of Elman NN air fuel ratio model is less than 0.5%, however, which of BP NN is more than 1%.

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Wang, Fang, Sai Tang, and Menggang Li. "Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market." Complexity 2021 (May 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/6641298.

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With a focus in the financial market, stock market dynamics forecasting has received much attention. Predicting stock market fluctuations is usually challenging due to the nonlinear and nonstationary time series of stock prices. The Elman recurrent network is renowned for its capability of dealing with dynamic information, which has made it a successful application to predicting. We developed a hybrid approach which combined Elman recurrent network with factorization machine (FM) technique, i.e., the FM-Elman neural network, to predict stock market volatility. In this paper, the Standard & Poor’s 500 Composite Stock Price (S&P 500) index, the Dow Jones industrial average (DJIA) index, the Shanghai Stock Exchange Composite (SSEC) index, and the Shenzhen Securities Component Index (SZI) were used to demonstrate the validity of our proposed FM-Elman model in time-series prediction. The results were compared with predictions obtained from the other two models which are basic BP neural network and the Elman neural network. Some experiments showed that the FM-Elman model outperforms others through different accuracy measures. Furthermore, the effects of volatility degree on prediction performance from different stock indexes were investigated. An interesting phenomenon had been found through some numerical experiments on the effects of different user-specified dimensions on the proposed FM-Elman neural network.

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12

Wang, Xiaowen, Ying Ma, and Wen Li. "The Prediction of Gold Futures Prices at the Shanghai Futures Exchange Based on the MEEMD-CS-Elman Model." SAGE Open 11, no. 1 (January 2021): 215824402110018. http://dx.doi.org/10.1177/21582440211001866.

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The Gold futures market is a complex nonlinear system with the prediction of the futures prices of gold, one of the core issues faced by investors. Compared with more traditional approaches, empirical mode decomposition (EMD) and artificial neural network are the more powerful tools with which to deal with nonlinear and nonstationary price problems. By introducing mirroring extension (ME), EMD, Cuckoo Search (CS) algorithm, and Elman neural network, this article constructs the mirroring extension empirical mode decomposition (MEEMD)-CS-Elman model to forecast the price of gold futures using gold future AU0 price data from August 29, 2013, to October 18, 2018, at the Shanghai Futures Exchange (SFE) in China. Empirical results show that Elman combined with EMD is superior to single Elman in performance. Moreover, there exists an obvious endpoint effect by applying EMD to the price of AU0. By introducing the ME method, the endpoint effect can be dealt with better. Furthermore, by introducing the CS algorithm to optimize the initial weights and biases for Elman, the constructed MEEMD-CS-Elman model achieves far more accurate prediction results compared with either the EMD-Elman or the MEEMD-Elman model in terms of performance criterion: mean absolute difference (MAD), mean absolute percentage error (MAPE), root-mean-square error (RMSE), and directional symmetry (DS). In particular, the DS indicator, which reflects rising and falling prices, tends to be more attractive for investors. The value of the DS indicator in the MEEMD-CS-Elman model reaches 0.75207, meaning that the proposed model predicts the directions of increasing and falling prices quite precisely. Hence, by applying the proposed model, investors can make more scientific and accurate decisions and better reduce their investment risks.

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13

Xu, Lan, and Yuting Zhang. "Quality Prediction Model Based on Novel Elman Neural Network Ensemble." Complexity 2019 (May 21, 2019): 1–11. http://dx.doi.org/10.1155/2019/9852134.

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In this paper, we propose a novel prediction algorithm based on an improved Elman neural network (NN) ensemble for quality prediction, thus achieving the quality control of designed products at the product design stage. First, the Elman NN parameters are optimized using the grasshopper optimization (GRO) method, and then the weighted average method is improved to combine the outputs of the individual NNs, where the weights are determined by the training errors. Simulations were conducted to compare the proposed method with other NN methods and evaluate its performance. The results demonstrated that the proposed algorithm for quality prediction obtained better accuracy than other NN methods. In this paper, we propose a novel Elman NN ensemble model for quality prediction during product design. Elman NN is combined with GRO to yield an optimized Elman network ensemble model with high generalization ability and prediction accuracy.

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14

Wei, Yan Ming, Hua Ping Li, and Hai Long Gao. "Information-Applied Technology with Nonlinear System Modeling Method of Elman Network Based on Particle Swarm Optimization." Advanced Materials Research 952 (May 2014): 307–10. http://dx.doi.org/10.4028/www.scientific.net/amr.952.307.

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In order to improve the modeling ability for nonlinear system, an Elman modeling method based on Particle Swarm Optimization (PSO) algorithm is proposed. It uses PSO algorithm to optimize the parameters of Elman network. The simulation result shows that the proposed hybrid method combined Elman with PSO algorithm has a good modeling performance with fast training rate for complex nonlinear system.

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15

Xiang Li, Guanrong Chen, Zengqiang Chen, and Zhuzhi Yuan. "Chaotifying linear Elman networks." IEEE Transactions on Neural Networks 13, no. 5 (September 2002): 1193–99. http://dx.doi.org/10.1109/tnn.2002.1031950.

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16

Zhang, Lin, Yi Min Wang, and Deng Feng Liu. "Calculating the Synthetic Efficiency of Hydroturbine Based on the BP Neural Network and Elman Neural Network." Applied Mechanics and Materials 457-458 (October 2013): 801–5. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.801.

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The synthetic efficiency of hydroturbine is the foundation and important basis of economic operation of hydropower plants. However, the traditional calculation methods have large amount of calculation, large errors and other problems. To solve the problems better, this paper introduces the Elman neural network and BP neural network to calculate the synthetic efficiency of hydroturbine. Comparing the results of the Elman neural network and the traditional BP neural network, the result shows that, Elman neural network is an effective way to improve the learning speed, effectively suppress the minimum defects that the traditional neural network is easily trapped in, and reduce the error. The result suggests that the Elman neural network has better nonlinear mapping capability than the BP neural network.

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17

Liu, Ruijun, Dapai Shi, and Chao Ma. "Real-Time Control Strategy of Elman Neural Network for the Parallel Hybrid Electric Vehicle." Journal of Applied Mathematics 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/596326.

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Through researching the instantaneous control strategy and Elman neural network, the paper established equivalent fuel consumption functions under the charging and discharging conditions of power batteries, deduced the optimal control objective function of instantaneous equivalent consumption, established the instantaneous optimal control model, and designs the Elman neural network controller. Based on the ADVISOR 2002 platform, the instantaneous optimal control strategy and the Elman neural network control strategy were simulated on a parallel HEV. The simulation results were analyzed in the end. The contribution of the paper is that the trained Elman neural network control strategy can reduce the simulation time by 96% and improve the real-time performance of energy control, which also ensures the good performance of power and fuel economy.

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18

Jia, Weikuan, Shanhao Mou, Jing Wang, Xiaoyang Liu, Yuanjie Zheng, Jian Lian, and Dean Zhao. "Fruit recognition based on pulse coupled neural network and genetic Elman algorithm application in apple harvesting robot." International Journal of Advanced Robotic Systems 17, no. 1 (January 1, 2020): 172988141989747. http://dx.doi.org/10.1177/1729881419897473.

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In order to improve the harvesting efficiency of apple harvesting robot, this article presents an apple recognition method based on pulse coupled neural network and genetic Elman neural network (GA-Elman). Firstly, we use pulse coupled neural network to segment the captured 150 images, respectively, and extract six color features of R, G, B, H, S, and I and 10 shape features of circular variance, density, the ratio of perimeter square to area, and Hu invariant moments of segmented images, and these 16 features are considered as the inputs of Elman neural network. In order to overcome some defects of Elman neural network, such as, trapping local minimum easily and determining the number of hidden neurons difficultly; in this article, genetic algorithm is introduced to optimize it, and new optimization way is designed, that is, the connection weights and number of hidden neurons separate encoding and evolving simultaneously, in the process of structural evolution at the same time the learning of connection weights is completed, and then the operating efficiency and recognition precision of Elman model are improved. In order to get more precision neural network, and avoid the influence of fruit recognition caused by branches or leaves shadow, apple along with branches and leaves is allowed to train. The results of experiments show that compared with the traditional back-propagation, Elman neural network, and other two recognition algorithms of obscured fruit. the genetic Elman neural network algorithm is the optimal method which successful training rate can reach to 100%, recognition rate of overlapping fruit and obscured fruit can reach to 88.67% and 93.64%, respectively, and the total recognition rate reaches to 94.88%.

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Zheng, Yanling, Xueliang Zhang, Xijiang Wang, Kai Wang, and Yan Cui. "Predictive study of tuberculosis incidence by time series method and Elman neural network in Kashgar, China." BMJ Open 11, no. 1 (January 2021): e041040. http://dx.doi.org/10.1136/bmjopen-2020-041040.

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ObjectivesKashgar, located in Xinjiang, China has a high incidence of tuberculosis (TB) making prevention and control extremely difficult. In addition, there have been very few prediction studies on TB incidence here. We; therefore, considered it a high priority to do prediction analysis of TB incidence in Kashgar, and so provide a scientific reference for eventual prevention and control.DesignTime series study.Setting Kashgar, ChinaKashgar, China.MethodsWe used a single Box-Jenkins method and a Box-Jenkins and Elman neural network (ElmanNN) hybrid method to do prediction analysis of TB incidence in Kashgar. Root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to measure the prediction accuracy.ResultsAfter careful analysis, the single autoregression (AR) (1, 2, 8) model and the AR (1, 2, 8)-ElmanNN (AR-Elman) hybrid model were established, and the optimal neurons value of the AR-Elman hybrid model is 6. In the fitting dataset, the RMSE, MAE and MAPE were 6.15, 4.33 and 0.2858, respectively, for the AR (1, 2, 8) model, and 3.78, 3.38 and 0.1837, respectively, for the AR-Elman hybrid model. In the forecasting dataset, the RMSE, MAE and MAPE were 10.88, 8.75 and 0.2029, respectively, for the AR (1, 2, 8) model, and 8.86, 7.29 and 0.2006, respectively, for the AR-Elman hybrid model.ConclusionsBoth the single AR (1, 2, 8) model and the AR-Elman model could be used to predict the TB incidence in Kashgar, but the modelling and validation scale-dependent measures (RMSE, MAE and MAPE) in the AR (1, 2, 8) model were inferior to those in the AR-Elman hybrid model, which indicated that the AR-Elman hybrid model was better than the AR (1, 2, 8) model. The Box-Jenkins and ElmanNN hybrid method therefore can be highlighted in predicting the temporal trends of TB incidence in Kashgar, which may act as the potential for far-reaching implications for prevention and control of TB.

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Belhaj Salah, L., and F. Fourati. "Systems Modeling Using Deep Elman Neural Network." Engineering, Technology & Applied Science Research 9, no. 2 (April 10, 2019): 3881–86. http://dx.doi.org/10.48084/etasr.2455.

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In this paper, the modeling of complex systems using deep Elman neural network architecture is improved. The emphasis is to retrieve better deep Elman structure that emulates perfectly such dynamic systems. To achieve this goal, sigmoid activation functions in the hidden and output layers nodes are chosen and data files on considered systems for modeling and validation steps are given. Simulation results prove the ability and the efficiency of a deep Elman neural network with two hidden layers in this task.

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Liu, Zai Wen, Xiao Yi Wang, and Qiao Mei Wu. "An Integrated Evaluative Function and Water Bloom Predicting and Prewarning System." Advanced Materials Research 476-478 (February 2012): 2427–34. http://dx.doi.org/10.4028/www.scientific.net/amr.476-478.2427.

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An integrated evaluative function and prediction model and prewarning system for water bloom in lakes based on Elman neural network is proposed in this paper, in which main influence factor of outbreak of water bloom is analyzed by rough set theory. The study of the function involves some aspects: algal average activation energy of photosynthesis, integrated nutritional status index, and transparency, which are considered from the microcosmic level, the macroscopic level and the intuitionistic level respectively. The values of the function are classified properly. Combined with the basic features of outbreak of water-bloom, Elman network is studied from the angles of theory and experiment and a water-bloom prewarning system in short term based on Elman network is established. The results of simulation and application show that: Elman neural network improves the algorithm of BP neural network, it has long-term prediction period, strong generalization ability， high prediction accuracy; and needs a small amount of sample and this model provides an efficient new way for short-term water bloom prediction, And approaching ability of Elman network is more superficial than common static networks and its velocity of convergence is faster.

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Wang, Qiang. "Intelligent Identification of Flow Regime Based on a Novel Neural Network." Applied Mechanics and Materials 635-637 (September 2014): 1715–18. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1715.

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A noveol neural network of Elman is typically dynamic recurrent neural network. A novel method of flow regime identification based on Elman neural network and wavelet packet decomposition is proposed in this paper. Above all, the collected pressure-difference fluctuation signals are decomposed by the four-layer wavelet packet, and the decomposed signals in various frequency bands are obtained within the frequency domain. Then the wavelet packet energy eigenvectors of flow regimes are established. At last the wavelet packet energy eigenvectors are input into Elman neural network and flow regime intelligent identification can be performed.

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Mohana Sundaram, N., and S. N. Sivanandam. "A hybrid elman neural network predictor for time series prediction." International Journal of Engineering & Technology 7, no. 2.20 (April 18, 2018): 159. http://dx.doi.org/10.14419/ijet.v7i2.20.12799.

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Artificial Neural Networks have become popular in the world of prediction and forecasting due to their nonlinear nonparametric adaptive-learning property. They become an important tool in data analysis and data mining applications. Elman neural network due to its recurrent nature and dynamic processing capabilities can perform the prediction process with a good range of accuracy. In this paper an Elman recurrent Neural Network is hybridised with a time delay called a tap delay line for time series prediction process to improve its performance. The Elman neural network with the time delay inputs is trained tested and validated using the solar sun spot time series data that contains the monthly mean sunspot numbers for a 240 year period having 2899 data values. The results confirm that the proposed Elman network hybridised with time delay inputs can predict the time series with more accurately and effectively than the existing methods.

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Gong, Xiao Lu, Zhi Jian Hu, Meng Lin Zhang, and He Wang. "Wind Power Forecasting Using Wavelet Decomposition and Elman Neural Network." Advanced Materials Research 608-609 (December 2012): 628–32. http://dx.doi.org/10.4028/www.scientific.net/amr.608-609.628.

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The relevant data sequences provided by numerical weather prediction are decomposed into different frequency bands by using the wavelet decomposition for wind power forecasting. The Elman neural network models are established at different frequency bands respectively, then the output of different networks are combined to get the eventual prediction result. For comparison, Elman neutral network and BP neutral network are used to predict wind power directly. Several error indicators are given to evaluate prediction results of the three methods. The simulation results show that the Elman neural network can achieve good results and that prediction accuracy can be further improved by using the wavelet decomposition simultaneously.

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Li, Qian, Yong Qin, Zi Yang Wang, Zhong Xin Zhao, Ming Hui Zhan, and Yu Liu. "Prediction of Urban Rail Transit Sectional Passenger Flow Based on Elman Neural Network." Applied Mechanics and Materials 505-506 (January 2014): 1023–27. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.1023.

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This paper based on the feature of Beijing urban rail transit sectional passenger flow, combined with Elman neural network. After carrying out modeling experiment many times, a reasonable forecast model about the prediction of urban rail transit sectional passenger flow was established. Then the Elman neural network model was used to predict the sectional passenger flow of Beijing Subway Line 1, from Xidan station to Fuxingmen Station. At last the output results was compared with that of BP neural network, the result shows that the Elman neural network is more precise and effective than the BP neural network in the prediction of urban rail transit sectional passenger flow.

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Lin, Chih-Hong, and Chih-Peng Lin. "HYBRID MODIFIED ELMAN NN CONTROLLER DESIGN ON PERMANENT MAGNET SYNCHRONOUS MOTOR DRIVEN ELECTRIC SCOOTER." Transactions of the Canadian Society for Mechanical Engineering 37, no. 4 (December 2013): 1127–45. http://dx.doi.org/10.1139/tcsme-2013-0096.

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The electric scooter driven by permanent magnet synchronous motor (PMSM) has nonlinear and time-varying characteristic. An accurate dynamic model is not easy to establish for electric scooter in the linear controller design. In order to conquer the above problem, a novel hybrid modified Elman neural network (NN) control scheme is proposed to control for electric scooter driven by PMSM. The proposed control system consists of a supervisor control, a modified Elman NN and a compensated control with adaptive law. Finally, the effectiveness of the proposed novel hybrid modified Elman NN control system is demonstrated in comparison with the PI controller from some experimental results.

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Li, Yang, Bai Qing Hu, Feng Zha, and Kai Long Li. "A Novel Compensation Method for FOG Temperature Drift Based on TUKF." Applied Mechanics and Materials 568-570 (June 2014): 405–10. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.405.

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Aiming at the problem of modeling and compensation of the fiber optic gyroscope (FOG) drift caused by temperature, a novel compensation method for FOG temperature drift based on transformed unscented Kalman filter (TUKF) is proposed. Elman network with faster convergence speed is used to modeling and TUKF algorithm is adopted to train the weights of Elman network, which effectively solves the problem of numerical instability. The results prove that the proposed method has higher precision compared with Elman network and IUKF network models. By using the TUKF algorithm, the root mean square errors (RMSE) are improved by 60% in temperature rise period and 50.5% in fall period.

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Becher, Karim Johannes, and David B. Leep. "The Elman-Lam-Krüskemper Theorem." ISRN Algebra 2011 (June 28, 2011): 1–8. http://dx.doi.org/10.5402/2011/106823.

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For a (formally) real field K, the vanishing of a certain power of the fundamental ideal in the Witt ring of K(-1) implies that the same power of the fundamental ideal in the Witt ring of K is torsion free. The proof of this statement involves a fact on the structure of the torsion part of powers of the fundamental ideal in the Witt ring of K. This fact is very difficult to prove in general, but has an elementary proof under an assumption on the stability index of K. We present an exposition of these results.

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29

Marcus, Gary F. "Reply to Seidenberg and Elman." Trends in Cognitive Sciences 3, no. 8 (August 1999): 289. http://dx.doi.org/10.1016/s1364-6613(99)01357-1.

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Harding, Thomas G. "Elman Rogers Service (1915-1996)." American Anthropologist 101, no. 1 (March 1999): 161–64. http://dx.doi.org/10.1525/aa.1999.101.1.161.

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Kennedy, Randall. "A Reply to Philip Elman." Harvard Law Review 100, no. 8 (June 1987): 1938. http://dx.doi.org/10.2307/1341193.

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Ahn, Hongchul, Hotak Hong, Jongho Nang, and Saejoon Kim. "Forecasting KOSPI using Elman network." MATEC Web of Conferences 54 (2016): 05007. http://dx.doi.org/10.1051/matecconf/20165405007.

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Jing, Shao Xue, and Wei Kuan Jia. "Study on Elman Neural Networks Algorithms Based on Factor Analysis." Applied Mechanics and Materials 511-512 (February 2014): 945–49. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.945.

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When we manipulate high dimensional data with Elman neural network, many characteristic variables provide enough information, but too many network inputs go against designing of the hidden-layer of the network and take up plenty of storage space as well as computing time, and in the process interfere the convergence of the training network, even influence the accuracy of recognition finally. Factor Analysis (FA) concentrates the information that is carried by numerous original indexes which form the index system, and then stores it to the factor, and can according to the precision that the actual problem needs, through controlling the number of the factors, to adjust the amount of the information. In this paper we make full use of the advantages of FA and the properties of Elman neural network structures to establish FA-Elman algorithm. The new algorithm reduces dimensions by FA, and carry on network training and simulation with low dimensional data that we get, which obviously simplifies the network structure, and in the process, improves the training speed and generalization capacity of the Elman neural network.

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Fan, Jieqing, Chao Liu, Yajing Lv, Jing Han, and Jian Wang. "A Short-Term Forecast Model of foF2 Based on Elman Neural Network." Applied Sciences 9, no. 14 (July 10, 2019): 2782. http://dx.doi.org/10.3390/app9142782.

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The critical frequency foF2 of the ionosphere F2 layer is one of the most important parameters of the ionosphere. Based on the Elman neural network (ENN), this paper constructs a single station forecasting model to predict foF2 one hour ahead. In order to avoid the network falling into local minimum, the model is optimized by the improved particle swarm optimization (IPSO). The input parameters used in the model include local time, seasonal information, solar cycle information and magnetic activity information. Data of the Wuhan Station from 2008 to 2016 were used to train and test the model. The prediction results of foF2 show that the root mean square error (RMSE) of the Elman neural network model is 4.30% lower than that of the back-propagation neural network (BPNN) model. The RMSE is further reduced by 8.92% after using the IPSO to optimize the model. This indicates that the Elman neural network model optimized by the improved particle swarm optimization is superior to the BP neural network and Elman neural network in the forecast of foF2 one hour ahead at Wuhan station.

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Feng, Yong Ming, Dong Lai Zhao, and Peng Qi Zhang. "Calculation of Compressor Characteristics Based on Elman Neural Network." Advanced Materials Research 500 (April 2012): 727–32. http://dx.doi.org/10.4028/www.scientific.net/amr.500.727.

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A calculation of an axial compressor characteristics is made based on Elman neural network. The experimental data provided by manufacturers are used for the neural network training.To establish the function model to obtain the pressure ratio and efficiency respectively. The result show that Elman neural network both have a good precision for prediction of interpolation and extrapolation.

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Su, Liyun, Li Deng, Wanlin Zhu, and Shengli Zhao. "Statistical Detection of Weak Pulse Signal under Chaotic Noise Based on Elman Neural Network." Wireless Communications and Mobile Computing 2020 (January 23, 2020): 1–12. http://dx.doi.org/10.1155/2020/9653586.

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Weak signal detection is a significant problem in modern detection such as mechanical fault diagnosis. The uniqueness of chaos and good learning ability of neural networks provide new ideas and framework for weak signal detection field. In this paper, Elman neural network is applied to detect and recover weak pulse signal in chaotic noise. For detection problem of weak pulse signal under chaotic noise, based on short-term predictability of chaotic observations, phase space reconstruction for observed signals is carried out. And Elman deep learning adaptive detection model (EDAD model) is established for weak pulse signal detection, and a hypothesis test is used to detect weak pulse signal from the prediction error. For the recovery of weak pulse signal under chaotic noise, a double-layer Elman deep neural network recovery model (DEDR model) is proposed, which is based on the Elman deep learning network model and single-point jump model for weak pulse signal, and it is optimized with goal of minimizing mean square prediction error of the Elman model. The profile least squares method is applied to estimate parameters of the DEDR model for difficult recovery of weak pulse signal because the DEDR model is essentially a semiparametric model with parametric and nonparametric parts. In the end, simulation experiments show that the model built in this paper can effectively detect and recover weak pulse signal in the background of chaotic noise.

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Chen, Ye. "Forecast of Short-Term Wind Power Based on GA Optimized Elman Neural Network." Applied Mechanics and Materials 536-537 (April 2014): 470–75. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.470.

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Due to the features of being fluctuant, intermittent, and stochastic of wind power, interconnection of large capacity wind farms with the power grid will bring about impact on the safety and stability of power systems. Based on the real-time wind power data, wind power prediction model using Elman neural network is proposed. At the same time in order to overcome the disadvantages of the Elman neural network for easily fall into local minimum and slow convergence speed, this paper put forward using the GA algorithm to optimize the weight and threshold of Elman neural network. Through the analysis of the measured data of one wind farm, shows that the forecasting method can improve the accuracy of the wind power prediction, so it has great practical value.

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Xie, Shao Bo, and Lang Wei. "Application of Elman Neural Networks to Predict Truck’s Operating Speed." Advanced Materials Research 694-697 (May 2013): 2846–49. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2846.

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Prediction of vehicular operating speed is critical to evaluate the design consistency of road alignment. Elman neural networks are proposed to predict the truck’s 85th percentile operating speed. A total of 190 samples are collected from the two-lane rural roads and two factors are considered as input variables to the model including the curve radius and longitudinal slope. 100 samples are applied for training the networks to get the prediction model and the other 90 samples are used for the model validation. Additionally, the Elman neural networks are compared with back-propagation neural networks and linear regression, and the results show that the Elman neural networks are prior to the other two approaches and can be regarded as an alternative to predict truck’s operating speed.

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Lv, Xiao Ren, Xuan Luo, Shi Jie Wang, and Rui Nie. "Short-Term Prediction on the Time Series of PCP Speed Based on Elman Neural Network." Advanced Materials Research 569 (September 2012): 749–53. http://dx.doi.org/10.4028/www.scientific.net/amr.569.749.

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Elman neural network is a classical kind of recurrent neural network. It is well suitable to predict complicated nonlinear dynamics system like progressing cavity pump (PCP) speed due to its greater properties of calculation and adaptation to time-varying with the comparison of BP neural network. This paper provides one method to create, predict, and decide the model of PCP speed based on Elman neural network. At the same time, short-term prediction is made on time series of PCP speed using this model. The results of the experiment show that the model owns higher precision, steadier forecasting effect and more rapid convergence velocity, displaying that this kind of model based on Elman neural network is feasible and efficient to predict short-term PCP speed.

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Wang, He Yi, and Xu Chang Yang. "Elman's Recurrent Neural Network Applied to Forecasting Algal Dynamic Variation in Gonghu Bay." Advanced Materials Research 779-780 (September 2013): 1352–58. http://dx.doi.org/10.4028/www.scientific.net/amr.779-780.1352.

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This paper describes the training, validation and application of recurrent neural network (RNN) models to computing the algal dynamic variation at three sites in Gonghu Bay of Lake Taihu in summer. The input variables of Elmans RNN were selected by means of the canonical correspondence analysis (CCA) and Chl_a concentration as output variable. Sequentially, the conceptual models for Elmans RNN were established and the Elman models were trained and validated on daily data set. The values of Chl_a concentration computed by the models were closely related to their respective values measured at the three sites. The correlation coefficient (R2) between the predicted Chl_a concentration by the model and the observed value were 0.86-0.92. And sensitivity analysis was performed to clarify the algal dynamic variation to the change of environmental factors. The results show that the CCA can efficiently ascertain appropriate input variables for Elmans RNN, the Elmans RNN can precisely forecast the Chl_a concentration at three different sites in Gonghu Bay of Lake Taihu in summer and sensitivity analysis validated the algal dynamic variation to the change of environmental factors which were selected by CCA.

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Zhang, Jing Gang. "Application of Gray Elman Neural Network to Predict the Gas Emission Amount." Advanced Materials Research 706-708 (June 2013): 1750–54. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.1750.

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The prediction of mine Gas Emission Amount is an important part of helping to make rational gas control measures. In order to improve the accuracy of mine gas emission prediction, this paper introduced the grey theory into the Elman artificial neural network theory, and combined the gray prediction model GM (1,1) with the Elman neural network model,established a gray Elman artificial neural network prediction model of gas emission, and carried on the simulation through software Matlab. Practice and experiment showed that this method compared well, and is superior to the traditional Grey prediction model, moreover this method also applied to the situation of original data was few or the historical data had transition. The forecasting results from this method can be more reliable and accurate, so it can instruct the practice accurately

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Zhao, Xinlong, Shuai Shen, Liangcai Su, and Xiuxing Yin. "Elman neural network–based identification of rate-dependent hysteresis in piezoelectric actuators." Journal of Intelligent Material Systems and Structures 31, no. 7 (February 25, 2020): 980–89. http://dx.doi.org/10.1177/1045389x20905987.

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Rate-dependent hysteresis nonlinearity in piezoelectric actuators severely limits micro- and nanoscale system performance. It is necessary to establish a dynamic model to describe the full behavior of rate-dependent hysteresis. In this article, the Elman neural network–based hysteresis model is developed for piezoelectric actuators. An improved dynamic hysteretic operator is proposed to transform the multi-valued mapping of hysteresis into one-to-one mapping on a newly constructed expanded input space. Then, Elman neural network incorporated with the improved dynamic hysteretic operator is utilized to approximate the behavior of rate-dependent hysteresis. The combination of Elman neural network and the improved dynamic hysteretic operator can dually embody the dynamic property and is capable of fully extracting the characteristics of rate-dependent hysteresis. The experimental results are presented to illustrate the potential of the proposed modeling technique.

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43

Wysocki, Antoni, and Maciej Ławryńczuk. "Elman neural network for modeling and predictive control of delayed dynamic systems." Archives of Control Sciences 26, no. 1 (March 1, 2016): 117–42. http://dx.doi.org/10.1515/acsc-2016-0007.

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The objective of this paper is to present a modified structure and a training algorithm of the recurrent Elman neural network which makes it possible to explicitly take into account the time-delay of the process and a Model Predictive Control (MPC) algorithm for such a network. In MPC the predicted output trajectory is repeatedly linearized on-line along the future input trajectory, which leads to a quadratic optimization problem, nonlinear optimization is not necessary. A strongly nonlinear benchmark process (a simulated neutralization reactor) is considered to show advantages of the modified Elman neural network and the discussed MPC algorithm. The modified neural model is more precise and has a lower number of parameters in comparison with the classical Elman structure. The discussed MPC algorithm with on-line linearization gives similar trajectories as MPC with nonlinear optimization repeated at each sampling instant.

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Liu, Bo, Qilin Wu, and Qian Cao. "An Improved Elman Network for Stock Price Prediction Service." Security and Communication Networks 2020 (September 3, 2020): 1–9. http://dx.doi.org/10.1155/2020/8824430.

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The rapid development of edge computing drives the rapid development of stock market prediction service in terminal equipment. However, the traditional prediction service algorithm is not applicable in terms of stability and efficiency. In view of this challenge, an improved Elman neural network is proposed in this paper. Elman neural network is a typical dynamic recurrent neural network that can be used to provide the stock price prediction service. First, the prediction model parameters and build process are analysed in detail. Then, the historical data of the closing price of Shanghai composite index and the opening price of Shenzhen composite index are collected for training and testing, so as to predict the prices of the next trading day. Finally, the experiment results validate that it is effective to predict the short-term future stock price by using the improved Elman neural network model.

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Leiviskä, Kauko. "Elman Network in Kappa Number Prediction." IFAC Proceedings Volumes 42, no. 19 (2009): 477–82. http://dx.doi.org/10.3182/20090921-3-tr-3005.00083.

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46

Ramo, Fawziya, and Alaa Mohamed. "Textures Recognition using Elman Neural Network." AL-Rafidain Journal of Computer Sciences and Mathematics 11, no. 1 (July 1, 2014): 97–108. http://dx.doi.org/10.33899/csmj.2014.163741.

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Ramya, V., V. Kavitha, and P. Sivagamasundhari. "Human Face Recognition using Elman Networks." International Journal of Engineering Trends and Technology 17, no. 6 (November 25, 2014): 293–96. http://dx.doi.org/10.14445/22315381/ijett-v17p260.

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48

Xiang Li, Zengqiang Chen, Zhuzhi Yuan, and Guanrong Chen. "Generating chaos by an Elman network." IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 48, no. 9 (2001): 1126–31. http://dx.doi.org/10.1109/81.948441.

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49

Lixin, Xu. "Approximation capability of Elman neural network." IFAC Proceedings Volumes 32, no. 2 (July 1999): 5313–16. http://dx.doi.org/10.1016/s1474-6670(17)56904-9.

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Asakawa, Shin-ichi. "Can we extend the Elman network?" Proceedings of the Annual Convention of the Japanese Psychological Association 76 (September 11, 2012): 3AMA37. http://dx.doi.org/10.4992/pacjpa.76.0_3ama37.

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