Reconstrução e reconhecimento de imagens binárias utilizando o algoritmo Máquina de Boltzmann

• Ano de defesa: 2016
• Autor(a) principal: Vale, Gustavo Bruno do
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Ubelândia; Brasil; Programa de Pós-graduação em Engenharia Elétrica
• Palavras-chave em Português: Engenharia elétrica; Reconhecimento de padrões; Redes neurais (Computação); Algoritmos; Máquina de Boltzmann; Reconhecimento de imagens; Redes Neurais Artificiais; Boltzmann Machine; Recognition of images; Pattern Recognition; Artificial Neural Networks; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufu.br/handle/123456789/17561; http://doi.org/10.14393/ufu.di.2016.366

The objective of this work is to use algorithms known as Boltzmann Machine to rebuild and classify patterns as images. This algorithm has a similar structure to that of an Artificial Neural Network but network nodes have stochastic and probabilistic decisions. This work presents the theoretical framework of the main Artificial Neural Networks, General Boltzmann Machine algorithm and a variation of this algorithm known as Restricted Boltzmann Machine. Computer simulations are performed comparing algorithms Artificial Neural Network Backpropagation with these algorithms Boltzmann General Machine and Machine Restricted Boltzmann. Through computer simulations are analyzed executions times of the different described algorithms and bit hit percentage of trained patterns that are later reconstructed. Finally, they used binary images with and without noise in training Restricted Boltzmann Machine algorithm, these images are reconstructed and classified according to the bit hit percentage in the reconstruction of the images. The Boltzmann machine algorithms were able to classify patterns trained and showed excellent results in the reconstruction of the standards code faster runtime and thus can be used in applications such as image recognition.