• JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
 
  Bookmark and Share
 
 
Doctoral Thesis
DOI
https://doi.org/10.11606/T.3.2001.tde-16072024-114825
Document
Author
Full name
Francisco Javier Ropero Peláez
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2001
Supervisor
Committee
Maruyama, Newton (President)
Araújo, Aluízio Fausto Ribeiro
Calôba, Luiz Pereira
Ferreira, Ademar
Kaminski, Paulo Carlos
Title in Portuguese
Redes neurais e lógica fuzzy sob a perspectiva de uma teoria algébrica da probabilidade.
Keywords in Portuguese
Algoritmos
Lógica Fuzzy
Redes neurais
Abstract in Portuguese
Nesta tese foi desenvolvida uma nova teoria Euclidiana da Probabilidade que permite visualizar os eventos estatísticos como vetores. Os conceitos de ângulo e projeção permitiram desenvolver um novo tipo de algoritmo de Gram-Schmidt para encontrar uma base de vetores ortogonais a partir de outros vetores quaisquer. Esta base de vetores pode ser uma base más reduzida quando os eixos são os chamados Componentes Principais. Um novo algoritmo de extração de Componentes Principais foi desenvolvido. Estes fundamentos matemáticos serviram para envasar de maneira diferente à lógica fuzzy e às redes neurais artificiais. Na área da lógica fuzzy estas equações fornecem um método diferente de desenhar analiticamente as funções de pertinência e de encontrar as regras composicionais de inferência. Na área das redes neurais permitiram o desenho e fácil entendimento de uma nova rede neural baseada na neuro-fisiologia do tálamo que extrai os Componentes Principais para lograr uma compressão eficiente da informação. Este tálamo artificial foi implementado em Matlab.
Title in English
Untitled in english
Keywords in English
Algorithms
Fuzzy Logic
Neural networks
Abstract in English
This thesis develops a new Theory of Probability that allows to understand statistical events as vectors in an Euclidean space. The concepts of angle and projection allowed to develop a new Gram-Schmidt-type algorithm for finding a basis of orthogonal vectors from any other set of vectors. This basis can be reduced when the axes are the so called Principal Components. A new algorithm for extracting the Principal Components was developed. These mathematical foundations served for grounding Fuzzy Logic and Neural Networks in a different way. Regarding Fuzzy Logic, these equations represent a different method for analytically designing membership functions and for finding the compositional inference rules. In the Neural Network field these foundations allowed the easy understanding of a new neural network based on the neuro-physiology of thalamus that extracts the Principal Components for achieving an efficient compression of information. This artificial thalamus was implemented in Matlab.
 
WARNING - Viewing this document is conditioned on your acceptance of the following terms of use:
This document is only for private use for research and teaching activities. Reproduction for commercial use is forbidden. This rights cover the whole data about this document as well as its contents. Any uses or copies of this document in whole or in part must include the author's name.
Publishing Date
2024-07-16
 
WARNING: Learn what derived works are clicking here.
All rights of the thesis/dissertation are from the authors
CeTI-SC/STI
Digital Library of Theses and Dissertations of USP. Copyright © 2001-2024. All rights reserved.