Otimizaaao De Redes Neurais Mlp
Mostrando 1-4 de 4 artigos, teses e dissertações.
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1. Uma anÃlise de otimizaÃÃo de redes neurais MLP por exames de partÃculas
This work presents a methodology for global optimization of MLP artificial neural networks. The main objective here is the simultaneous optimization of architectures and connection weights of MLP networks, aiming good classification performance for most data sets. The simultaneous adjustment of architectures and connection weights of MLP networks represents
Publicado em: 2007
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2. Abordagem hÃbrida para otimizaÃÃo de redes neurais artificiais para previsÃo de sÃries temporais
This thesis proposes a new hybrid approach which combines simulated annealing and standard error backpropagation for optimizing Multi Layer Perceptron Neural Networks (MLP) for time series prediction. This approach named ANNSATS (Artificial Neural Networks and Simulated Annealing for Time Series Forecasting) starts from an initial topology fully connected ne
Publicado em: 2007
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3. Optimization methods for the definition of MLP neural network architectures and weights / Optimization methods for the definition of MLP neural network architectures and weights / MÃtodos de otimizaÃÃo para definiÃÃo de arquiteturas e pesos de redes neurais MLP / MÃtodos de otimizaÃÃo para definiÃÃo de arquiteturas e pesos de redes neurais MLP
This dissertation proposes modifications in the Yamazaki method for the simultaneous optimization of Multilayer Perceptron (MLP) network weights and architectures. The main objective is to propose a set of modifications with respective validations aimed at creating a more efficient optimization process. The optimization hybrid algorithm is based on the simul
Publicado em: 2005
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4. Uma metodologia para otimizaÃÃo de arquitetura e pesos de redes neurais
Este trabalho propÃe uma metodologia para a otimizaÃÃo global de redes neurais. O objetivo à a otimizaÃÃo simultÃnea de arquiteturas e pesos de redes Multi-Layer Perceptron (MLP), com o intuito de gerar topologias com poucas conexÃes e alto desempenho de classificaÃÃo para qualquer conjunto de dados. A otimizaÃÃo simultÃnea de arquiteturas e pes
Publicado em: 2004