Artificial Neural Networks With Dynamic Topology
Mostrando 1-5 de 5 artigos, teses e dissertações.
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1. GeraÃÃo de trajetÃrias de estados por mapas auto-organizÃveis com topologia dinÃmica
Este trabalho apresenta um novo modelo de Rede Neural Artificial de Topologia DinÃmica chamado State Trajectory Generator (STRAGEN) capaz de gerar trajetÃrias de estados a partir do mapeamento do espaÃo de estados de um sistema. O modelo permite a utilizaÃÃo de diferentes critÃrios para a composiÃÃo de uma trajetÃria Ãtima de acordo com o domÃnio
Publicado em: 2008
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2. Processamento de perfis metabólicos / Metabolic profiles processing
During the past 30-years, Biochemical System Theory (BST) has been provided a concrete foundation for the study of the dynamic biological systems, e.g. S-systems models for reverse engineering of metabolic networks (Savageau, 1969; Savageau, 1970; Voit, 2000). One of the remarkable characteristics of these models is its parameters not only quantify the inter
Publicado em: 2007
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3. Aprendizado supervisionado usando redes neurais construtivas.
Constructive neural learning is a neural learning model that does not assume a fixed network topology before training begins. The main characteristic of this learning model is the dynamic construction of the networks hidden layers that occurs simultaneously with training. This work investigates three topics related to constructive neural learning namely algo
Publicado em: 2006
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4. 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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5. Previsão de carga no periodo de demanda de ponta utilizando redes neurais artificiais
The ability to accurately predict the system load is vital to the efficient, economic, and secure operation and planning of a power system. This work investigates the use of artificial neural networks for short and very short-term load peak demand forecasting. Two forecasting algorithms are tested and evaluated based on their precision and computational load
Publicado em: 1996