16 resultados para neural network model
Filtro por publicador
- Aberdeen University (1)
- Abertay Research Collections - Abertay University’s repository (1)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (2)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (6)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (2)
- Aquatic Commons (2)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (3)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (2)
- Aston University Research Archive (44)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (7)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (6)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (22)
- Boston University Digital Common (47)
- Brock University, Canada (1)
- Bulgarian Digital Mathematics Library at IMI-BAS (17)
- CaltechTHESIS (1)
- Cambridge University Engineering Department Publications Database (40)
- CentAUR: Central Archive University of Reading - UK (83)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (44)
- Cochin University of Science & Technology (CUSAT), India (8)
- CORA - Cork Open Research Archive - University College Cork - Ireland (1)
- Deakin Research Online - Australia (180)
- Digital Archives@Colby (1)
- Digital Commons - Michigan Tech (1)
- Digital Commons @ DU | University of Denver Research (1)
- Digital Commons @ Winthrop University (1)
- Digital Commons at Florida International University (8)
- Digital Peer Publishing (1)
- Duke University (3)
- Greenwich Academic Literature Archive - UK (3)
- Helda - Digital Repository of University of Helsinki (4)
- Indian Institute of Science - Bangalore - Índia (47)
- Instituto de Engenharia Nuclear, Brazil - Carpe dIEN (2)
- Instituto Politécnico de Bragança (1)
- Instituto Politécnico do Porto, Portugal (5)
- Massachusetts Institute of Technology (2)
- Memorial University Research Repository (1)
- National Center for Biotechnology Information - NCBI (4)
- Nottingham eTheses (4)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (6)
- Publishing Network for Geoscientific & Environmental Data (2)
- QSpace: Queen's University - Canada (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (94)
- Queensland University of Technology - ePrints Archive (50)
- RDBU - Repositório Digital da Biblioteca da Unisinos (1)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (2)
- Repositório digital da Fundação Getúlio Vargas - FGV (1)
- Repositório Digital da UNIVERSIDADE DA MADEIRA - Portugal (1)
- Repositório Institucional da Universidade Federal do Rio Grande do Norte (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (77)
- Research Open Access Repository of the University of East London. (5)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (1)
- SAPIENTIA - Universidade do Algarve - Portugal (13)
- School of Medicine, Washington University, United States (1)
- Universidad Autónoma de Nuevo León, Mexico (1)
- Universidad de Alicante (4)
- Universidad del Rosario, Colombia (2)
- Universidad Politécnica de Madrid (16)
- Universidade Complutense de Madrid (1)
- Universidade Federal do Pará (3)
- Universidade Federal do Rio Grande do Norte (UFRN) (9)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (1)
- Université de Montréal (1)
- Université de Montréal, Canada (1)
- University of Michigan (1)
- University of Queensland eSpace - Australia (27)
- University of Southampton, United Kingdom (1)
- WestminsterResearch - UK (4)
Resumo:
Abstract This paper presents a new method to extract knowledge from existing data sets, that is, to extract symbolic rules using the weights of an Artificial Neural Network. The method has been applied to a neural network with special architecture named Enhanced Neural Network (ENN). This architecture improves the results that have been obtained with multilayer perceptron (MLP). The relationship among the knowledge stored in the weights, the performance of the network and the new implemented algorithm to acquire rules from the weights is explained. The method itself gives a model to follow in the knowledge acquisition with ENN.