Relevant bibliographies by topics / Elman Recurrent Neural Network(Elman)

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Academic literature on the topic 'Elman Recurrent Neural Network(Elman)'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Elman Recurrent Neural Network(Elman)"

1

Wutsqa, Dhoriva Urwatul, and Anisa Nurjanah. "Breast Cancer Classification Using Fuzzy Elman Recurrent Neural Network." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (2019): 946–53. http://dx.doi.org/10.5373/jardcs/v11sp11/20193119.

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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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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 (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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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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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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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

Jiwa Permana, Agus Aan, and Widodo Prijodiprodjo. "Sistem Evaluasi Kelayakan Mahasiswa MagangMenggunakan Elman Recurrent Neural Network." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 8, no. 1 (2014): 37. http://dx.doi.org/10.22146/ijccs.3494.

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AbstrakJaringan Syaraf Tiruan (JST) dapat digunakan untuk memecahkan permasalahan tertentu seperti prediksi, klasifikasi, pengolahan data, dan robotik.Berdasarkan paparan tersebut, sehingga dalam penelitian ini mencoba menerapkan JST untuk menangani permasalahan dalam program magang yang sedang dihadapi dalam upaya untuk meningkatkan kompetensi, pengalaman, serta melatih softskill mahasiswa.Sistem yang dikembangkan dapat digunakan untuk mengevaluasi kelayakan mahasiswa dalam program magang ke luar daerah dengan menerapkan Elman Recurrent Neural Network (ERNN), sehingga dapat memberikan informasi yang akurat kepada pihak jurusan untuk menentukan keputusan yang tepat.Struktur Elman dipilih karena dapat membuat iterasi jauh lebih cepat sehingga memudahkan proses konvergensi. Adapun metode pembelajaran yang digunakan adalah Backpropagation ThroughTime dengan model epochwise training mode. Sistem diimplementasikan dengan menggunakan bahasa pemrograman C# dengan basis data MySQL. Vektor input yang digunakan terdiri dari 11 variabel. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan akan cepat mengalami konvergen dan mampu mencapai nilai error paling optimal (minimum error) apabila menggunakan 1 hidden layer dengan jumlah neuron 20 unit. Akurasi terbaik dapat diperoleh dengan menggunakan LR sebesar 0.01 dan momentum 0.85 dimana akurasi rata-rata dalam pengujian mencapai 87.50%. Kata kunci—Evaluasi, Kelayakan, Jaringan Syaraf Tiruan (JST), Elman Recurrent Neural Network, Magang Abstract Artificial Neural Network (ANN) can be used to solve specific problems such as prediction, classification, data processing, and robotics. Based on the exposure, so in this study tried to apply neural networks to handle problems in apprentice program facing in an effort to increase the competence, experience and soft skills training students. The system developed can be used to evaluate the students in the apprentice program to other regions by applying the Elman Recurrent Neural Network (ERNN), so it can provide accurate information to the department to determine appropriate decisions. Elman structure was chosen because it can be create much more rapidly iterations so as to facilitate the convergence process. The learning method used is Backpropagation Through Time with model epochwise training mode. The system is implemented using the C # programming language with a MySQL database. Input vector used consists of 11 variables. The results showed that the developed system will rapidly converge and can reach optimal error value (minimum error) when using one hidden layer with 20 units number of neurons. Best accuracy can be obtained using the LR of 0.01 and momentum 0.85 which average accuracy reaches 87.50% in testing. Keywords—Evaluation, Feasibility, Artificial Neural Network (ANN), Elman Recurrent Neural Network, Apprenticeship
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8

Aribowo, Widi. "ELMAN-RECURRENT NEURAL NETWORK FOR LOAD SHEDDING OPTIMIZATION." SINERGI 24, no. 1 (2020): 29. http://dx.doi.org/10.22441/sinergi.2020.1.005.

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Load shedding plays a key part in the avoidance of the power system outage. The frequency and voltage fluidity leads to the spread of a power system into sub-systems and leads to the outage as well as the severe breakdown of the system utility. In recent years, Neural networks have been very victorious in several signal processing and control applications. Recurrent Neural networks are capable of handling complex and non-linear problems. This paper provides an algorithm for load shedding using ELMAN Recurrent Neural Networks (RNN). Elman has proposed a partially RNN, where the feedforward connections are modifiable and the recurrent connections are fixed. The research is implemented in MATLAB and the performance is tested with a 6 bus system. The results are compared with the Genetic Algorithm (GA), Combining Genetic Algorithm with Feed Forward Neural Network (hybrid) and RNN. The proposed method is capable of assigning load releases needed and more efficient than other methods.
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Radjabaycolle, Jefri, and Reza Pulungan. "PREDIKSI PENGGUNAAN BANDWIDTH MENGGUNAKAN ELMAN RECURRENT NEURAL NETWORK." BAREKENG: Jurnal Ilmu Matematika dan Terapan 10, no. 2 (2016): 127–35. http://dx.doi.org/10.30598/barekengvol10iss2pp127-135.

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Jaringan Syaraf Tiruan (JST) sering dipakai dalam menyelesaikan permasalahan tertentu seperti prediksi, klasifikasi, dan pengolahan data. Berdasarkan hal tersebut, dalam penelitian ini mencoba menerapkan JST untuk menangani permasalahan dalam prediksi penggunaan bandwidth. Sistem yang dikembangkan dapat digunakan untuk memprediksi pengunaan bandwidth dengan menerapkan Elman Recurrent Neural Network (ERNN). Struktur Elman dipilih karena dapat membuat iterasi jauh lebih cepat sehingga memudahkan proses konvergensi.. Vektor input yang digunakan menggunakan windows size. Hasil penelitian dengan menggunakan target error sebesar 0.001 menunjukkan nilai MSE terkecil yaitu pada windows size 11 dengan nilai 0.002833. Kemudian dengan menggunakan 13 neuron pada hidden layer diperoleh nilai error paling optimal (minimum error) sebesar 0.003725.
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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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