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Algoritmos evolutivos multiobjectivo de redes neuronais de espigões

Algoritmos evolutivos multiobjectivo de redes neuronais de espigões

Abdulrazak Yahya Saleh

41,51 €
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Editorial:
KS OmniScriptum Publishing
Año de edición:
2025
Materia
Informática: cuestiones generales
ISBN:
9786208557478
41,51 €
IVA incluido
Disponible

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Sobre IBD

As redes neuronais de estimulação (SNN) desempenham um papel essencial nos problemas de classificação. Embora existam muitos modelos de SNN, a Evolving Spiking Neural Network (ESNN) é amplamente utilizada em muitos trabalhos de investigação recentes. Os algoritmos evolutivos, principalmente a evolução diferencial (DE), têm sido utilizados para melhorar o algoritmo ESNN. No entanto, muitos problemas de otimização do mundo real incluem vários objectivos contraditórios. Neste livro, a Harmony Search (HS) e a abordagem memética foram utilizadas para melhorar o desempenho da MOO com ESNN. Consequentemente, foi aplicada a Evolução Diferencial Multi-Objetivo de Pesquisa Harmónica Memetic com Rede Neural Evolutiva (MEHSMODE-ESNN) para melhorar a estrutura da ESNN e as taxas de precisão. São utilizados conjuntos de dados padrão da aprendizagem automática da UCI para avaliar o desempenho deste modelo híbrido multiobjectivo melhorado. Os resultados experimentais provaram que a Evolução Diferencial Multi-Objetivo da Pesquisa Harmónica Memetic com Rede Neural Evolutiva (MEHSMODE-ESNN) dá melhores resultados em termos de precisão e estrutura da rede.

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