Relevant bibliographies by topics / Recurrent Elman neural network

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Recurrent Elman neural network'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Recurrent Elman neural network"

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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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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 input
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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 conn
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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 informa
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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 analy
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Wang, Jie, Jun Wang, Wen Fang, and Hongli Niu. "Financial Time Series Prediction Using Elman Recurrent Random Neural Networks." Computational Intelligence and Neuroscience 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/4742515.

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In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN),
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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 me
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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 &
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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
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Lin, Chih-Min, and Enkh-Amgalan Boldbaatar. "Autolanding Control Using Recurrent Wavelet Elman Neural Network." IEEE Transactions on Systems, Man, and Cybernetics: Systems 45, no. 9 (2015): 1281–91. http://dx.doi.org/10.1109/tsmc.2015.2389752.

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Dissertations / Theses on the topic "Recurrent Elman neural network"

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Gomes, Leonaldo da Silva. "Redes Neurais Aplicadas à InferÃncia dos Sinais de Controle de Dosagem de Coagulantes em uma ETA por FiltraÃÃo RÃpida." Universidade Federal do CearÃ, 2012. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=8105.

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Considerando a importÃncia do controle da coagulaÃÃo quÃmica para o processo de tratamento de Ãgua por filtraÃÃo rÃpida, esta dissertaÃÃo propÃe a aplicaÃÃo de redes neurais artificiais para inferÃncia dos sinais de controle de dosagem de coagulantes principal e auxiliar, no processo de coagulaÃÃo quÃmica em uma estaÃÃo de tratamento de Ãgua por filtraÃÃo rÃpida. Para tanto, foi feito uma anÃlise comparativa da aplicaÃÃo de modelos baseados em redes neurais do tipo: alimentada adiante focada atrasada no tempo (FTLFN); alimentada adiante atrasada no tempo distribuÃda (DTLFN); recorrente de Elma
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Křepský, Jan. "Rekurentní neuronové sítě v počítačovém vidění." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-237029.

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The thesis concentrates on using recurrent neural networks in computer vision. The theoretical part describes the basic knowledge about artificial neural networks with focus on a recurrent architecture. There are presented some of possible applications of the recurrent neural networks which could be used for a solution of real problems. The practical part concentrates on face recognition from an image sequence using the Elman simple recurrent network. For training there are used the backpropagation and backpropagation through time algorithms.
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Дудник, Алексей Валентинович. "Оптимальные системы управления переходными процессами энергосберегающих объектов с переменными параметрами". Thesis, НТУ "ХПИ", 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/22099.

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Диссертация на соискание ученой степени кандидата технических наук по специальности 05.13. 03 – системы и процессы управления. – Национальный технический университет "Харьковский политехнический институт", Харьков, 2016. Диссертация посвящена решению научно-практической задачи усовершенствования оптимальной по затратам энергии системы управления. В диссертационной работе рассмотрен метод оптимального управления в линейной системе при квадратичном критерии качества с учётом ограничений на управляемые координаты, применительно к разомкнутой системе. Показано, что существует шесть вариантов алго
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Дудник, Олексій Валентинович. "Оптимальні системи керування перехідними процесами енергозаощаджуючих об'єктів зі змінними параметрами". Thesis, НТУ "ХПІ", 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/22091.

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Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.13. 03 – системи та процеси керування. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2016. Дисертація присвячена вирішенню науково-практичної задачі вдосконалення оптимальною за витратами енергії системи керування. У дисертаційній роботі розглянуто метод оптимального керування в лінійній системі при квадратичному критерії якості з урахуванням обмежень на керовані координати, стосовно до розімкнутої системи. Показано, що існує шість варіантів алгоритмів оптимального керува
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Tekin, Mim Kemal. "Vehicle Path Prediction Using Recurrent Neural Network." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166134.

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Vehicle Path Prediction can be used to support Advanced Driver Assistance Systems (ADAS) that covers different technologies like Autonomous Braking System, Adaptive Cruise Control, etc. In this thesis, the vehicle’s future path, parameterized as 5 coordinates along the path, is predicted by using only visual data collected by a front vision sensor. This approach provides cheaper application opportunities without using different sensors. The predictions are done by deep convolutional neural networks (CNN) and the goal of the project is to use recurrent neural networks (RNN) and to investigate t
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Wen, Tsung-Hsien. "Recurrent neural network language generation for dialogue systems." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/275648.

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Language is the principal medium for ideas, while dialogue is the most natural and effective way for humans to interact with and access information from machines. Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact on usability and perceived quality. Many commonly used NLG systems employ rules and heuristics, which tend to generate inflexible and stylised responses without the natural variation of human language. However, the frequent repetition of identical output forms can quickly make dialogue become tedious for most real-world users.
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He, Jian. "Adaptive power system stabilizer based on recurrent neural network." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0008/NQ38471.pdf.

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Gangireddy, Siva Reddy. "Recurrent neural network language models for automatic speech recognition." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28990.

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The goal of this thesis is to advance the use of recurrent neural network language models (RNNLMs) for large vocabulary continuous speech recognition (LVCSR). RNNLMs are currently state-of-the-art and shown to consistently reduce the word error rates (WERs) of LVCSR tasks when compared to other language models. In this thesis we propose various advances to RNNLMs. The advances are: improved learning procedures for RNNLMs, enhancing the context, and adaptation of RNNLMs. We learned better parameters by a novel pre-training approach and enhanced the context using prosody and syntactic features.
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Bopaiah, Jeevith. "A recurrent neural network architecture for biomedical event trigger classification." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/73.

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A “biomedical event” is a broad term used to describe the roles and interactions between entities (such as proteins, genes and cells) in a biological system. The task of biomedical event extraction aims at identifying and extracting these events from unstructured texts. An important component in the early stage of the task is biomedical trigger classification which involves identifying and classifying words/phrases that indicate an event. In this thesis, we present our work on biomedical trigger classification developed using the multi-level event extraction dataset. We restrict the scope of o
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Amartur, Sundar C. "Competitive recurrent neural network model for clustering of multispectral data." Case Western Reserve University School of Graduate Studies / OhioLINK, 1995. http://rave.ohiolink.edu/etdc/view?acc_num=case1058445974.

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Books on the topic "Recurrent Elman neural network"

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Yi, Zhang, and K. K. Tan. Convergence Analysis of Recurrent Neural Networks (Network Theory and Applications). Springer, 2003.

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Book chapters on the topic "Recurrent Elman neural network"

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Krichene, Emna, Youssef Masmoudi, Adel M. Alimi, Ajith Abraham, and Habib Chabchoub. "Forecasting Using Elman Recurrent Neural Network." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53480-0_48.

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Bilski, Jarosław, and Jacek Smola̧g. "Parallel Realisation of the Recurrent Elman Neural Network Learning." In Artifical Intelligence and Soft Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13232-2_3.

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Nawi, Nazri Mohd, M. Z. Rehman, Norhamreeza Abdul Hamid, Abdullah Khan, Rashid Naseem, and Jamal Uddin. "Optimizing Weights in Elman Recurrent Neural Networks with Wolf Search Algorithm." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51281-5_2.

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Wysocki, Antoni, and Maciej Ławryńczuk. "Two- and Three-Layer Recurrent Elman Neural Networks as Models of Dynamic Processes." In Challenges in Automation, Robotics and Measurement Techniques. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29357-8_15.

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Zhang, Zhong-Yuan. "Recurrent Neural Network." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_418.

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Ayyadevara, V. Kishore. "Recurrent Neural Network." In Pro Machine Learning Algorithms. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3564-5_10.

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Nakayama, Hideki. "Recurrent Neural Network." In Computer Vision. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63416-2_855.

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Nakayama, Hideki. "Recurrent Neural Network." In Computer Vision. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-03243-2_855-1.

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Kanagachidambaresan, G. R., Adarsha Ruwali, Debrup Banerjee, and Kolla Bhanu Prakash. "Recurrent Neural Network." In Programming with TensorFlow. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57077-4_7.

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Long, Liangqu, and Xiangming Zeng. "Recurrent Neural Network." In Beginning Deep Learning with TensorFlow. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7915-1_11.

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Conference papers on the topic "Recurrent Elman neural network"

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Mat Darus, I. Z., M. O. Tokhi, and S. Z. Mohd. Hashim. "Non-Linear System Identification of Flexible Plate Structures Using Neural Networks." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58200.

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This paper investigates the utilisation of feedforward and recurrent neural networks for dynamic modelling of a flexible plate structure. Neuro-modelling techniques are used for non-parametric identification of the flexible plate structure based on one-step-ahead prediction. A multi layer perceptron (MLP) and Elman neural networks are designed to characterise the dynamic behaviour of the flexible plate. Results of the modelling techniques are validated through a range of tests including input/output mapping, training and test validation, mean-squared error and correlation tests. Results are pr
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Nikolaev, Nikolay Y., Derrick Mirikitani, and Evgueni Smirnov. "Unscented grid filtering and elman recurrent networks." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596830.

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N., Siddarameshwara, Anup Yelamali, and Kshitiz Byahatti. "Electricity Short Term Load Forecasting Using Elman Recurrent Neural Network." In 2010 International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom). IEEE, 2010. http://dx.doi.org/10.1109/artcom.2010.44.

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Feng, Chenghao, Zheng Zhao, Zhoufeng Ying, Jiaqi Gu, David Z. Pan, and Ray T. Chen. "Compact Design of On-chip Elman Optical Recurrent Neural Network." In CLEO: Applications and Technology. OSA, 2020. http://dx.doi.org/10.1364/cleo_at.2020.jth2b.8.

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Aytenfsu, Samuel A., Asrat M. Beyene, and Tameru H. Getaneh. "Controlling the Interior of Greenhouses using Elman Recurrent Neural Network." In 2020 Fourth World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4). IEEE, 2020. http://dx.doi.org/10.1109/worlds450073.2020.9210373.

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Toha, S. F., and M. O. Tokhi. "MLP and Elman recurrent neural network modelling for the TRMS." In 2008 7th IEEE International Conference on Cybernetic Intelligent Systems (CIS). IEEE, 2008. http://dx.doi.org/10.1109/ukricis.2008.4798969.

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Yan, Jihong, and Pengxiang Wang. "Blade Material Fatigue Assessment Using Elman Neural Networks." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-43311.

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Material degradation evaluation and life prediction of major components such as blades, rotors, valves of steam turbines not only guarantees reliable, efficient and continuous operation of electric plants, but also offers the promise of substantially reducing the cost of repair and replacement of defective parts, and may even result in saving lives. In this paper, a recurrent neural network based strategy was developed for material degradation assessment and fatigue damage propagation prediction. Two Elman Neural Networks were developed for fatigue severity assessment and trend prediction corr
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Dillak, Rocky Yefrenes, Sumartini Dana, and Marthen Beily. "Face recognition using 3D GLCM and Elman Levenberg recurrent Neural Network." In 2016 International Seminar on Application for Technology of Information and Communication (ISemantic). IEEE, 2016. http://dx.doi.org/10.1109/isemantic.2016.7873829.

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Wang, Limin, Xuming Han, and Ming Li. "Dynamic recurrent Elman neural network based on immune clonal selection algorithm." In Fourth International Conference on Digital Image Processing (ICDIP 2012), edited by Mohamed Othman, Sukumar Senthilkumar, and Xie Yi. SPIE, 2012. http://dx.doi.org/10.1117/12.956430.

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Dillak, Rocky Yefrenes, and Petrisia Widyasari Sudarmadji. "Cervical Cancer Classification Using Elman Recurrent Neural Network and Genetic Algorithm." In 2021 5th International Conference on Informatics and Computational Sciences (ICICoS). IEEE, 2021. http://dx.doi.org/10.1109/icicos53627.2021.9651852.

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Reports on the topic "Recurrent Elman neural network"

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Brabel, Michael J. Basin Sculpting a Hybrid Recurrent Feedforward Neural Network. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada336386.

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Bodruzzaman, M., and M. A. Essawy. Iterative prediction of chaotic time series using a recurrent neural network. Quarterly progress report, January 1, 1995--March 31, 1995. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/283610.

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BARKHATOV, NIKOLAY, and SERGEY REVUNOV. A software-computational neural network tool for predicting the electromagnetic state of the polar magnetosphere, taking into account the process that simulates its slow loading by the kinetic energy of the solar wind. SIB-Expertise, 2021. http://dx.doi.org/10.12731/er0519.07122021.

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The auroral activity indices AU, AL, AE, introduced into geophysics at the beginning of the space era, although they have certain drawbacks, are still widely used to monitor geomagnetic activity at high latitudes. The AU index reflects the intensity of the eastern electric jet, while the AL index is determined by the intensity of the western electric jet. There are many regression relationships linking the indices of magnetic activity with a wide range of phenomena observed in the Earth's magnetosphere and atmosphere. These relationships determine the importance of monitoring and predicting ge
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Mohanty, Subhasish, and Joseph Listwan. Development of Digital Twin Predictive Model for PWR Components: Updates on Multi Times Series Temperature Prediction Using Recurrent Neural Network, DMW Fatigue Tests, System Level Thermal-Mechanical-Stress Analysis. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1822853.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti
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