skip to main content
Idioma:
Primo Search
Clear Search Box
Search in: Busca Geral
Busca Avançada
Busca por Índices

Clustered Echo State networks for signal denoising and frequency filtering

Oliveira Junior, Laercio De

Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto 2020-11-05

Acesso online. A biblioteca também possui exemplares impressos.

  • Título:
    Clustered Echo State networks for signal denoising and frequency filtering
  • Autor: Oliveira Junior, Laercio De
  • Orientador: Liang, Zhao
  • Assuntos: Reservoir Computing; Redes Neurais Artificiais; Redes Complexas; Redes Com Clusters; Echo State Networks; Complex Networks; Clustered Networks; Reservoir Computing; Artificial Neural Networks
  • Notas: Dissertação (Mestrado)
  • Descrição: This dissertation aims to study a type of Artificial Neural Networks (ANNs), known as Reservoir Computing, specifically, the Echo State Networks (ESNs). ESNs are Recurrent Neural Networks (RNNs), which make input-output mapping through a high dimensional nonlinear projection, called reservoir. In a classic ESN, the internal connection matrix of the reservoir usually is formed by an Erdös-Rényi random graph. Recent studies have also investigated Clustered ESNs (CESNs), which replaces the random network inside the reservoir by a clustered network. Both types of ESNs have been applied to time series prediction problems. In this work, an ESN with a clustered Barabási-Albert network (Barabási-Albert CESN), and a deep ESN with clustered reservoir layers (Deep CESNs) are designed. Moreover, we propose to apply ESNs in two new different tasks: the frequency filtering problem and the noise filtering problem of time series. We also compare the performance of the classical ESN and its various extensions in these two tasks. Numerical results show that the proposed ESNs (Barabási-Albert CESN and Deep CESNs) outperform the classical ESN, indicating that the organization of reservoirs in clustered or layered networks can improve the learning performance of ESNs.
  • DOI: 10.11606/D.59.2020.tde-28022021-205755
  • Editor: Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto
  • Data de criação/publicação: 2020-11-05
  • Formato: Adobe PDF
  • Idioma: Inglês
Links
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.