Recurrent Neural Networks
Mostrando 13-24 de 30 artigos, teses e dissertações.
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13. Identificação de sistemas "on-line", otimização e controle avançado com o filtro de Kalman estendido / On line system identification, advanced control and optimization with the (Extended) Kalman filter
In the continuing competition between it will be more and more necessary to optimize current chemical processes in real time. To be able to optimize a plant in real time, there have to be various aspects to be fulfilled, such as measurement, reliability of the measurement and prediction of the process behaviour. In this work some of the aspects of such an ad
Publicado em: 2006
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14. Application of time-delay neural and recurrent neural networks for the identification of a hingeless helicopter blade flapping and torsion motions
System identification consists of the development of techniques for model estimation from experimental data, demanding no previous knowledge of the process. Aeroelastic models are directly influence of the benefits of identification techniques, basically because of the difficulties related to the modelling of the coupled aero- and structural dynamics. In thi
Journal of the Brazilian Society of Mechanical Sciences and Engineering. Publicado em: 2005-06
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15. Recurrent neural networks for prediction of short and long memory time series / Modelos de redes neurais recorrentes para previsão de series temporais de memorias curta e longa
Forecasting of time series is a topic of great interest nowadays. To do so, the data generating process needs to be estimated with a good degree of accuracy. In the last years, artificial neural networks are becoming more important in the statistical community. The more basic structure of a neural network, the feedforward neural nets, without feedback, can b
Publicado em: 2005
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16. Modelagem computacional de dados e controle inteligente no espaço de estado / State space computational data modelling and intelligent control
This study presents contributions for state space multivariable computational data modelling with discrete time invariant as well as with time varying linear systems. A proposal for Deterministic-Estocastica Modelling of noisy data, MOESP_AOKI Algorithm, is made. We present proposals forsolving the Discrete-Time Algebraic Riccati Equation as well as the asso
Publicado em: 2005
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17. Redes neurais recorrentes aplicadas à assimilação de dados em dinâmica não-linear / Recurrent neural networks applayed to data assimilation on non-linear dynamic
Neste trabalho aplica-se as Redes Neurais (RN) Perceptron de Múltiplas Camadas (PMC), Funções de Base Radial (FBR), Elman (RN-E) e Jordan (RN-J) num contexto de Assimilação de Dados em dinâmica não-linear. Avalia-se a eficiência das RN em emular o filtro de Kalman (FK) e a possível aplicabilidade desta técnica a problemas de dimensão maior, como p
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 16/12/2004
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18. Extração de conhecimento a partir de redes reurais recorrentes / knowledge extraction from recurrent neural networks
ln this work a method ofknowledge extraction from Recurrent Neural Network is proposed. Express formally the knowledge stored inside an Artificial Neural Network is a great challenge, because such knowledge has to be reformulated and presented by simple and understandable means. Three symbolic formats are presented for the representation of this knowledge: F
Publicado em: 2004
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19. Redes neurais recorrentes aplicadas à assimilação de dados em dinâmica não-linear / Recurrent neural networks applayed to data assimilation on non-linear dynamic
Neste trabalho aplica-se as Redes Neurais (RN) Perceptron de Múltiplas Camadas (PMC), Funções de Base Radial (FBR), Elman (RN-E) e Jordan (RN-J) num contexto de Assimilação de Dados em dinâmica não-linear. Avalia-se a eficiência das RN em emular o filtro de Kalman (FK) e a possível aplicabilidade desta técnica a problemas de dimensão maior, como p
Publicado em: 2004
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20. Redes neurais recorrentes aplicadas à assimilação de dados em dinâmica não-linear / Recurrent neural networks applayed to data assimilation on non-linear dynamic
Neste trabalho aplica-se as Redes Neurais (RN) Perceptron de Múltiplas Camadas (PMC), Funções de Base Radial (FBR), Elman (RN-E) e Jordan (RN-J) num contexto de Assimilação de Dados em dinâmica não-linear. Avalia-se a eficiência das RN em emular o filtro de Kalman (FK) e a possível aplicabilidade desta técnica a problemas de dimensão maior, como p
Publicado em: 2004
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21. STRUCTURES AND ALGORITHMS FOR MULTIUSER DETECTION AND INTERFERENCE SUPRESSION IN DS-CDMA SYSTEMS / ESTRUTURAS E ALGORITMOS PARA DETECÇÃO MULTIUSUÁRIO E SUPRESSÃO DE INTERFERÊNCIA EM SISTEMAS DS-CDMA
This thesis presents new structures and algorithms for multiuser detection and interference suppression in DS-CDMA systems. Structures based on recurrent neural networks are investigated for decision feedback receivers and adaptive algorithms are developed for combatting multiple access interference and intersymbol interference. New algorithms based on the m
Publicado em: 2004
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22. Symbolic processing in neural networks
In this paper we show that programming languages can be translated into recurrent (analog, rational weighted) neural nets. Implementation of programming languages in neural nets turns to be not only theoretical exciting, but has also some practical implications in the recent efforts to merge symbolic and sub symbolic computation. To be of some use, it should
Journal of the Brazilian Computer Society. Publicado em: 2003-04
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23. Redes neurais fuzzy aplicadas em identificação e controle de sistemas
This work compares the performance of neural fuzzy, neural network and fuzzy systems, to model and control non-linear dynamical systems. Due to the need of temporal representations, two recurrent neural fuzzy networks are proposed based on an hybrid static neural fuzzy architecture. Temporal processing is induced by local and global recurrence in the hidden
Publicado em: 2003
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24. Equalização não-linear de canais de comunicação. / Non-linear equalization on communication channels.
É investigado o uso de redes neurais aplicadas à equalização de canais de comunicação, sendo consideradas três tipos de redes: MLP (Multilayer Perceptron), RBF (Radial Basis Function) e RNN (Recurrent Neural Network). Os equalizadores não-lineares baseados nestas redes foram comparados com o equalizador linear transversal e com os equalizadores ótim
Publicado em: 2001