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Redes neurais para regressão uni- e multivariada
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Palavras-chave

Redes neurais artificiais
Regressão
Algoritmo genético
Recozimento simulado
Retropropagação
Otimização

Como Citar

1.
Pereira GC, Custodio R. Redes neurais para regressão uni- e multivariada. Rev. Chemkeys [Internet]. 9º de setembro de 2021 [citado 18º de abril de 2024];3(00):e021003. Disponível em: https://econtents.bc.unicamp.br/inpec/index.php/chemkeys/article/view/15880

Resumo

Redes Neurais Artificiais têm ganhado notoriedade na aproximação de funções uni e multivariadas em virtude a alta capacidade aproximativa desse tipo de modelo. Neste artigo é apresentada uma descrição dos modelos de regressão baseados em redes neurais juntamente com os algoritmos comumente usados para otimizá-los. A performance deste tipo de modelo é exemplificada através da aproximação de uma função univariada que relaciona a fração em mol na fase líquida de um dos componentes de uma mistura água-acetona com sua fração em mol na fase de vapor. O desempenho do modelo é, ainda, comparado com o desempenho de outros modelos baseados em métodos de regressão clássicos utilizados para solucionar o mesmo problema. Ao final do texto, é apresentado o código PYTHON para a criação do modelo de rede neural discutido aqui.

https://doi.org/10.20396/chemkeys.v3i00.15880
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Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Copyright (c) 2021 Gabriel César Pereira, Rogério Custodio

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