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knowledge extraction from recurrent neural networks

dc.contributor.advisorCechin, Adelmo Luis
dc.contributor.authorSimon, Denise Regina Pechmannpt_BR
dc.date.accessioned2015-03-05T13:53:45Z
dc.date.accessioned2022-09-22T19:05:09Z
dc.date.available2015-03-05T13:53:45Z
dc.date.available2022-09-22T19:05:09Z
dc.date.issued2004-05-11
dc.identifier.urihttps://hdl.handle.net/20.500.12032/56747
dc.description.abstractln this work a method ofknowledge extraction from Recurrent Neural Network is proposed. Express formally the knowledge stored inside an Artificial Neural Network is a great challenge, because such knowledge has to be reformulated and presented by simple and understandable means. Three symbolic formats are presented for the representation of this knowledge: Fuzzy Finite Automata, Markov Chains and Deterministic Finite Automata. For the knowledge extraction used in this work, each space region of the neuron activity is associated to a meaning. The considered method uses clusterization of the neural space in order to obtain the automata states, using the K-means algorithm and the fuzzy clustering. The knowledge acquisition is made using Recurrent Neural Networks to learn the behavior of the two non linear dynamic systems and, from the trained nets, to extract the states and possible automata transitions. The dynamic systems are the lnverse Pendulum system and the Lorenz system. The presented extraction method waen
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_BR
dc.languagept_BRpt_BR
dc.publisherUniversidade do Vale do Rio do Sinospt_BR
dc.rightsopenAccesspt_BR
dc.subjectlógica difusapt_BR
dc.subjectfinite state machineen
dc.titleExtração de conhecimento a partir de redes reurais recorrentespt_BR
dc.titleknowledge extraction from recurrent neural networksen
dc.typeDissertaçãopt_BR


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