Relevant bibliographies by topics / Elman

Contents

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

Academic literature on the topic 'Elman'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Elman"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Elman"

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Hedenström, Patrik. "Olika arkitekturer för artificiella neurala nätverk i bilspel : En jämförelse av arkitekturerna feedforward, Elman och ESCN." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-10995.

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Detta arbete utvärderar ANN-arkitekturerna feedforward, Elman och ESCN då de används för att styra en bil i en enkel 2D-simulering. Nätverken tränas av en evolutionär algoritm som använder nätverkens vikter som genom för dess individer. Syftet med arbetet är att se om arkitekturerna presterar olika bra. Simuleringens komplexitet, i form av halka och sladd, samt banans svårighetsgrad varieras för att se vilka arkitekturer som klarar vilka komplexa problem bäst och var de eventuellt brister. Ett program utvecklades som testade de olika fallen och resultatet visade att Elman presterade sämst, speciellt då komplexiteten ökade, och ESCN presterade lite bättre än feedforward. Varför Elman presterade sämre fick inget svar i detta arbete, och ESCN använde sitt minne på ett sätt som skulle kunna vara värt att titta vidare på. Framtida arbete skulle kunna vara att ta reda på orsakerna till de ovanliga beteendena som uppstod samt att genomföra mer utförliga tester.
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Pasquotto, Jorge Luís Durgante. "Previsão de séries temporais no varejo brasileiro: uma investigação comparativa da aplicação de redes neurais recorrentes de Elman." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/12/12139/tde-24022011-180352/.

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Neste trabalho foi explorada a aplicação de redes neurais recorrentes simples, também conhecidas como Redes de Elman, na previsão de três séries temporais mensais do varejo de bens e serviços no Brasil. As variáveis destas séries estão relacionadas com a demanda de produtos farmacêuticos, adubos, e tráfego aéreo. As previsões com Redes de Elman foram comparadas com as realizadas por modelos lineares sazonais obtidos através da metodologia de Box-Jenkins.
In this work we explored the application of simple recurrent neural networks, also known as Elman networks, in the prediction of three series of retail goods and services in Brazil. The series are formed by variables related to the monthly demand for pharmaceuticals, fertilizers and domestic air traffic. The forecast with Elman networks were compared with those performed by seasonal linear models obtained by Box-Jenkins methodology.
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Sousa, Ana Paula de. "ANÁLISE COMPARATIVA DE MÉTODOS DE PREVISÃO DE SÉRIES TEMPORAIS ATRAVÉS DE MODELOS ESTATÍSTICOS E REDE NEURAL ARTIFICIAL." Pontifícia Universidade Católica de Goiás, 2012. http://localhost:8080/tede/handle/tede/2468.

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Made available in DSpace on 2016-08-10T10:40:27Z (GMT). No. of bitstreams: 1 ANA PAULA DE SOUSA.pdf: 965882 bytes, checksum: a3647999f994441f4537855527b52292 (MD5) Previous issue date: 2012-03-09
The objective of this study was to compare statistical methods and artificial intelligence to the problem of time series forecasting using Holt-Winters, Box-Jenkins and the Elman neural network. The models were used to predict one step ahead of the price of ethanol in the state of Goias and compared using measures of specific errors. At the end, the results indicated that all three techniques were competitive in terms of predicting one step ahead especially the statistical models appeared to be the most suitable methods in terms of balance between performance and complexity.
O objetivo deste trabalho foi comparar os métodos de estatística e de inteligência artificial para o problema da previsão de séries temporais através de Holt-Winters, Box- Jenkins e a rede neural de Elman. Os modelos foram utilizados para previsão um passo a frente dos preços do etanol no estado de Goiás e comparados através medidas de erros específicas. Ao final, os resultados indicaram que todos os métodos se mostraram competitivos em termos de predição um passo à frente, destacando-se os modelos estatísticos como os mais adequados em termos de parcimônia entre desempenho e complexidade.
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Taco, Pastor Willy Gonzales. "Redes neurais artificiais aplicadas na modelagem individual de padrões de viagens encadeadas a pé." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/18/18137/tde-18092015-163322/.

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O objetivo deste trabalho foi desenvolver um modelo para reconhecer e reproduzir padrões de viagens encadeadas a pé. O processo de modelagem foi conduzido através da aplicação das técnicas das Redes Neurais Artificiais (RNAs), utilizando-se de uma rede estática MLP e de rede dinâmica Elman. A análise do desempenho do modelo foi baseada nos dados de uma pesquisa de Origem-Destino realizada, em 1987, pelo METRÔ-SP na Região Metropolitana de São Paulo. Na modelagem foi fixado o modo de viagem a pé, e, na abordagem seqüencial, padrões de viagens individuais foram representados em termos de dois componentes: duração da viagem e tipo de atividades. A análise foi realizada partindo da classificação geral e específica para cada segmento do encadeamento de viagens, o que permitiu a comparação dos resultados entre padrões de viagens observados e os reproduzidos pelas redes. Na classificação geral, cinco dos padrões previstos com maior freqüência pelas RNAs representaram em média 58,9% dos indivíduos no conjunto de dados usado para testar o desempenho do modelo. Para o vetor de duas e quatro viagens, as redes neurais reproduziram 50% das durações de viagem e 90% das atividades, tais como Trabalho e Escola. Embora esses resultados não pareçam muito robustos, não significa que eles estejam errados. As porcentagens acima representam a probabilidade de uma pessoa realizar viagens com aquelas durações ou tipo de atividades.
The main objective of this work was to develop a model for recognizing and reproduzing trip-chaining patterns by walk. The process of modeling was conducted applying the techniques of Artificial Neural Networks (ANNs), by using one of the static networks MLP and the Elman dynamic network. The analysis of the performance of the model was based on the origin-destination home-interview survey carried out by METRÔ-SP in São Paulo Metropolitan Area in 1987. The mode of trip by walk was fixed in the model, and, in the sequential approach, individual travel patterns were represented in terms of two components: trip duration and activity type. The analysis was accomplished starting from the general and specific classifications for each segment of the chained trips, which allowed the comparison of the results between the observed travel patterns and reproduced ones through ANNs. In general classification, 5 of the patterns most frequently predicted by the ANNs represented 58.9% of the individuals in the dataset used for testing the model performance. For the vectors of two and four trips, the neural networks reproduced 50% of trip durations and 90% of the activities, such as work and school. Although those results seem not so robust, it does not mean that they are wrong. The percentages above represent the probability of a person making trips with those durations or type of activities.
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Carn, Cheril, and cheril Carn@dsto defence gov au. "The inverse determination of aircraft loading using artificial neural network analysis of structural response data with statistical methods." RMIT University. Aerospace, Mechanical and Manufacturing Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080109.090600.

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An artificial Neural Network (ANN) system has been developed that can analyse aircraft flight data to provide a reconstruction of the aerodynamic loads experienced by the aircraft during flight, including manoeuvre, buffet and distributed loading. For this research data was taken from the International Follow-On Structural Test Project (IFOSTP) F/A-18 fatigue test conducted by the Royal Australian Air Force and Canadian Forces. This fatigue test involved the simultaneous application of both manouevre and buffet loads using airbag actuators and shakers. The applied loads were representative of the actual loads experienced by an FA/18 during flight tests. Following an evaluation of different ANN types an Ellman network with three linear layers was selected. The Elman back-propagation network was tested with various parameters and structures. The network was trained using the MATLAB 'traingdx' function with is a gradient descent with momentum and adaptive learning rate back-propagation algorithm. The ANN was able to provide a good approximation of the actual manoeuvre or buffet loads at the location where the training loads data were recorded even for input values which differ from the training input values. In further tests the ability to estimate distributed loading at locations not included in the training data was also demonstrated. The ANN was then modified to incorporate various methods for the calculation and prediction of output error and reliability Used in combination and in appropriate circumstances, the addition of these capabilities significantly increase the reliability, accuracy and therefore usefulness of the ANN system's ability to estimate aircraft loading.To demonstrate the ANN system's usefulness as a fatigue monitoring tool it was combined with a formulae for crack growth analysis. Results inficate the ANN system may be a useful fatigue monitoring tool enabling real time monitoring of aircraft critical components using existing strain gauge sensors.
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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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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 Elman (ERN) e auto-regressiva nÃo-linear com entradas exÃgenas (NARX). Da anÃlise comparativa, o modelo baseado em redes NARX apresentou melhores resultados, evidenciando o potencial do modelo para uso em casos reais, o que contribuirà para a viabilizaÃÃo de projetos desta natureza em estaÃÃes de tratamento de Ãgua de pequeno porte.
Considering the importance of the chemical coagulation control for the water treatment by direct filtration, this work proposes the application of artificial neural networks for inference of dosage control signals of principal and auxiliary coagulant, in the chemical coagulation process in a water treatment plant by direct filtration. To that end, was made a comparative analysis of the application of models based on neural networks, such as: Focused Time Lagged Feedforward Network (FTLFN); Distributed Time Lagged Feedforward Network (DTLFN); Elman Recurrent Network (ERN) and Non-linear Autoregressive with exogenous inputs (NARX). From the comparative analysis, the model based on NARX networks showed better results, demonstrating the potential of the model for use in real cases, which will contribute to the viability of projects of this nature in small size water treatment plants.
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Ziriakus, Jennifer [Verfasser], Fritz Elmar [Akademischer Betreuer] Kühn, and Kai-Olaf [Akademischer Betreuer] Hinrichsen. "Ruthenium katalysierte Umvinylierung / Jennifer Ziriakus. Gutachter: Fritz Elmar Kühn ; Kai-Olaf Hinrichsen. Betreuer: Fritz Elmar Kühn." München : Universitätsbibliothek der TU München, 2012. http://d-nb.info/1035502801/34.

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Sauter, Elmar [Verfasser]. "Willensfreiheit und deterministisches Chaos / Elmar Sauter." Karlsruhe : KIT Scientific Publishing, 2013. http://www.ksp.kit.edu.

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Lippus, Urve. "Elmar Arro's letters to Karl Leichter." Internationale Arbeitsgemeinschaft für die Musikgeschichte in Mittel- und Osteuropa an der Universität Leipzig, 2005. https://ul.qucosa.de/id/qucosa%3A15950.

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Among the correspondence of Karl Leichter (TMM, Department of Music: M 159) there are 18 letters from Elmar Arro, written in Vienna from 1972 to 1982. Some of them are very short and practical - for years Arro planned to visit his home-land and in 1981 he finally succeeded, together with his wife he spent a few days in Riga and Tallinn. But several letters contain interesting and sometimes bitter reflections about the situation of East-European studies in the West, particularly in Germany.
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Books on the topic "Elman"

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Mischa Elman and the romantic style. Chur Switzerland: Harwood Academic Publishers, 1990.

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Shoshannat Yaʻaqov: Jewish and Iranian studies in honor of Yaakov Elman. Leiden: Brill, 2012.

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L, Carver Raymond, and Ferguson Milton, eds. Bud Stewart, Michigan's legendary lure maker: A biography of Elman "Bud" Stewart. Hillsdale, MI: Ferguson Communications, 1990.

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With all deliberate speed: The life of Philip Elman : an oral history memoir. Ann Arbor, Mich: The University of Michigan Press, 2004.

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Kremer, S. Lillian. Witness through the imagination: Ozick, Elman, Cohen, Potok, Singer, Epstein, Bellow, Steiner, Wallant, Malamud : Jewish-American Holocaust literature. Detroit: Wayne State University Press, 1989.

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Karakurt, Deniz. Elma: A Traditional Folk Story. Istanbul: Cinius, 2009.

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Elma. Cağaloğlu, İstanbul: Sel Yayıncılık, 2001.

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Trenkwalder, Elmer. Elmar Trenkwalder.--. Wien: Galerie Krinzinger, 1989.

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Andrews, Lyn. Ellan Vannin. London: Headline, 1991.

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1948-, Rojas Elmar, and Sullivan Edward J, eds. Elmar Rojas. [Guatemala City]: Armitano Editores, 1993.

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Book chapters on the topic "Elman"

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Langford, John, Xinhua Zhang, Gavin Brown, Indrajit Bhattacharya, Lise Getoor, Thomas Zeugmann, Thomas Zeugmann, et al. "Elman Network." In Encyclopedia of Machine Learning, 311. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_246.

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Hammer, Barbara. "Generalization of Elman networks." In Lecture Notes in Computer Science, 409–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0020189.

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di Tollo, Giacomo, and Marianna Lyra. "Elman Nets for Credit Risk Assessment." In New Economic Windows, 147–67. Milano: Springer Milan, 2010. http://dx.doi.org/10.1007/978-88-470-1778-8_8.

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Ding, Shifei, Weikuan Jia, Chunyang Su, Xinzheng Xu, and Liwen Zhang. "PCA-Based Elman Neural Network Algorithm." In Advances in Computation and Intelligence, 315–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-92137-0_35.

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Rothkrantz, L. J. M., and D. Nollen. "Speech Recognition Using Elman Neural Networks." In Text, Speech and Dialogue, 146–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48239-3_26.

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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, 488–97. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53480-0_48.

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Liu, Zhongqiang, Li Zhang, Chunxiao Zhang, Xiangfei Kong, and Anan Shen. "Transformer Fault Diagnosis Based on Elman Network." In Advances in Intelligent Systems and Computing, 479–86. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25128-4_60.

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Belhaj Salah, Latifa, and Fathi Fourati. "Deep Elman Neural Network for Greenhouse Modeling." In Smart Innovation, Systems and Technologies, 271–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21005-2_26.

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Rajasekaran, P., R. Prabakaran, and R. Thanigaiselvan. "Implementation of Elman Backprop for Dynamic Power Management." In Communications in Computer and Information Science, 232–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19263-0_28.

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Rabhi, Besma, Habib Dhahri, Adel M. Alimi, and Fahd A. Alturki. "Grey Wolf Optimizer for Training Elman Neural Network." In Advances in Intelligent Systems and Computing, 380–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52941-7_38.

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Conference papers on the topic "Elman"

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Song, Qing, Yeng Chai Soh, and Lei Zhao. "A robust extended Elman backpropagation algorithm." In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178829.

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Samek, David. "Elman Neural Networks In Model Predictive Control." In 23rd European Conference on Modelling and Simulation. ECMS, 2009. http://dx.doi.org/10.7148/2009-0577-0581.

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Wulandari, D. P., Y. K. Suprapto, and M. H. Purnomo. "Gamelan music onset detection using Elman Network." In 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA). IEEE, 2012. http://dx.doi.org/10.1109/cimsa.2012.6269604.

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Meng, Bo. "Improved Elman neural network and its application." In 2018 Chinese Control And Decision Conference (CCDC). IEEE, 2018. http://dx.doi.org/10.1109/ccdc.2018.8407152.

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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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Araghi, Leila Fallah, and Hamed Shah Hosseini. "Robust stability of Fuzzy Elman Neural Network." In 2009 IEEE International Conference on Granular Computing (GRC). IEEE, 2009. http://dx.doi.org/10.1109/grc.2009.5255173.

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Zhang, YiNan, and AiJun Ning. "Elman network voting system for cyclic system." In 2011 IEEE 2nd International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2011. http://dx.doi.org/10.1109/icsess.2011.5982232.

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Huang, JianPing, JianHua Han, and Yuan Luo. "Host Load Forecasting by Elman Neural Networks." In 2012 International Conference on Control Engineering and Communication Technology (ICCECT). IEEE, 2012. http://dx.doi.org/10.1109/iccect.2012.149.

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Loukeris, N., Y. Boutalis, S. Livanis, A. Arampatzis, and L. Maltoudoglou. "Hybrid Jordan Elman nets in portfolio selection." In 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, 2015. http://dx.doi.org/10.1109/iisa.2015.7387996.

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Crisan, Marius. "Phoneme Generation with Elman-type Neural Networks." In AASRI Winter International Conference on Engineering and Technology (AASRI-WIET 2013). Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/wiet-13.2013.6.

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Reports on the topic "Elman"

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Bickford, Mark. Event Logic Assistant (Elan). Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada487443.

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Wong, C., and L. Collins. TECHNICAL EQUIVALENCE BETWEEN PERKIN-ELMER DRCe AND ELAN 6000 FOR THE ANALYSIS OF 238U IN URINE BIOASSAY SAMPLES. Office of Scientific and Technical Information (OSTI), September 2007. http://dx.doi.org/10.2172/924967.

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Tippetts, Greg P. Supplement Analysis for the Transmission System Vegetation Management Program FEIS (DOE/EIS-0285/SA 107, Elma-Cosmopolis #1. Office of Scientific and Technical Information (OSTI), September 2002. http://dx.doi.org/10.2172/824756.

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