Hopfield Network
Mostrando 1-12 de 22 artigos, teses e dissertações.
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1. Hopfield Neural Network-Based Algorithm Applied to Differential Scanning Calorimetry Data for Kinetic Studies in Polymorphic Conversion
A general kinetic equation to simulate differential scanning calorimetry (DSC) data was employed along this work. Random noises are used to generate a thousand data, which are considered to evaluate the performance of Levenberg-Marquardt (LM) and a Hopfield neural network (HNN) based algorithm in the fitting process. The HNN-based algorithm showed better res
J. Braz. Chem. Soc.. Publicado em: 2020-07
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2. Ion-polymer interaction analysis: an inversion of NMR spin echo experimental data
A methodology for ion-polymer interaction estimation is discussed in the present work. This method is based on the inversion of experimental spin echo NMR data using Hopfield neural network to retrieve transverse relaxation time distributions. The adopted model systems consist of aqueous solutions of poly (ethylene oxide), molar mass 1500, 4000 and 35000 γ
Brazilian Journal of Physics. Publicado em: 2010-12
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3. Investigando o desempenho de redes neurais de Hopfield modificadas
Investiga-se a dinâmica da rede neural de tempo discreto proposta por Hopfield considerando duas modificações: uma que representa o efeito do mal de Alzheimer na topologia da rede e outra levando em conta que há uma segunda camada de neurônios. O mal de Alzheimer é modelado retirando-se neurônios e distribuindo-se os pesos das sinapses correspondentes
Publicado em: 2009
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4. Abordagem neuro-genética para mapeamento de problemas de conexão em otimização combinatória / Neurogenetic approach for mapping connection problems in combinatorial optimization
Due to applicability constraints involved with the algorithms for solving combinatorial optimization problems, systems based on artificial neural networks and genetic algorithms are alternative methods for solving these problems in an efficient way. The genetic algorithms must its popularity to make possible cover nonlinear and extensive search spaces. On th
Publicado em: 2009
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5. Uma arquitetura neuro-genética para otimização não-linear restrita / Neuro-genetic architecture for constrained nonlinear optimization
Systems based on artificial neural networks and genetic algorithms are an alternative method for solving systems optimization problems. The genetic algorithms must its popularity to make possible cover nonlinear and extensive search spaces. Artificial neural networks have high processing rates due to the use of a massive number of simple processing elements
Publicado em: 2007
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6. Memória associativa em redes neurais realimentadas / Associative memory in feedback neural networks
Nessa dissertação, é investigado o armazenamento e a recuperação de padrões de forma biologicamente inspirada no cérebro. Os modelos estudados consistiram de redes neurais realimentadas, que tentam modelar certos aspectos dinâmicos do funcionamento do cérebro. Em particular, atenção especial foi dada às Redes Neurais Celulares, que constituem uma
Publicado em: 2004
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7. Aspectos de mapas caóticos acoplados para processamento de informações / Aspects of chaotic coupled maps for information processing
A globally coupled map (GCM) model is a network of interconnected chaotic elements. In this work we investigate models based in the GCM and in the Hopfield network. Through modifications, like changing the local dynamics of the GCM (S-GCM) and adding self-feedback to the processing element of the Hopfield network, it s possible to use the models as an assoc�
Publicado em: 2002
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8. Detecção e diagnostico de falhas em sistemas dinamicos utilizando redes neurais e logica nebulosa
Fault detection and diagnosis methods have been intensively studied lately, as a result of the demand for systems of greater reliability. In this work, computational intelligence methods were adopted, in a configuration that uses artificial neural networks and fuzzy logic for monitoring dynamic systems represented by state-space models of adequate dimension.
Publicado em: 1999
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9. Uma abordagem neuro- nebulosa para otimização de sistema e indentificação robusta
The ability of artificial neural networks to solve complex and diversified problems make them attractive for application in many áreas of engineering and science. A neural network is basically composed of many simple processing elements with a high degree of connectivity among them. This thesis presents an architecture of artificial neural network to apply
Publicado em: 1997
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10. Estimação parametrica robusta atraves de redes neurais artificiais
Artificial Neural Networks can achieve high computation rates by employing a massive number of simple processing elements with a high degree of connectivity between these elements. Neural networks with feedback connections provide a computing model to solve a rich class of optimization problems. This dissertation presents an application of Hopfield s Neural
Publicado em: 1995
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11. Modification of the mean-square error principle to double the convergence speed of a special case of Hopfield neural network used to segment pathological liver color images
BioMed Central.
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12. Synchronous neural activity in scale-free network models versus random network models
Synchronous firing peaks at levels greatly exceeding background activity have recently been reported in neocortical tissue. A small subset of neurons is dominant in a large fraction of the peaks. To investigate whether this striking behavior can emerge from a simple model, we constructed and studied a model neural network that uses a modified Hopfield-type d
National Academy of Sciences.