Programa de Pós-Graduação em Engenharia Elétrica
PPGEE - UTFPR - PG

NOTÍCIAS

Minicurso dia 29/5 14h: On Global Nonlinear Dynamics for Engineering Design and System Safety

Palestra dia 15/5 14h: Gaussian Adaptive PID Controller

Palestra dia 8/5 14h: Fundamentals, Challenges and Opportunities of Self-Driving Cars

Palestra dia 8/5 14h45: Artifical Neural Networks

Palestra dia 27/3 14h: Electric Vehicle Battery-Ultracapacitor Energy System Optimization

Palestra dia 20/3 14h: Exploring nonlinear dynamics in 2D parameter space

Resultado preliminar Edital 03/2018 Credenciamento de docente PPGEE

Resultado final Edital 02/2018 Seleção de bolsistas

Candidatos selecionados (final) Edital 33/2017

Inscrições de alunos externos

Calendário acadêmico PPGEE 2018/1

Instruções para matrícula 2018/1

Horário 1º semestre 2018

Novo checklist para entrega da versão final das dissertações

Professores do PPGEE aprovam projetos na Chamada Universal MCTI/CNPq no. 14/2014

ALUNO DO PPGEE APROVADO NO ELAP PROGRAM DO CANADÁ

ALUNO DO PPGEE REALIZA ESTÁGIO DE MESTRADO NO G2ELAB EM GRENOBLE, FRANÇA

Pesquisadores franceses G2ELab e CNRS visitam o PPGEE

Palestra dia 8/5 14h45: Artifical Neural Networks

(Mai/2018) dia 8 de maio (terça), às 14h45, no auditório do DAELE, será apresentada a palestra "Artifical Neural Networks" apresentada pelo prof. Hugo Valadares Siqueira, professor da UTFPR-PG.

SHORT BIOGRAPHY:

Hugo Valadares Siqueira received the bachelor degree in Electrical Engineering from São Paulo State University-Brazil (2006), Msc. degree (2009) and PhD degree (2013) in Electrical Engineering from University of Campinas - Brazil. In 2014 he finalized his first postdoctoral stage at University of Campinas-Brazil and in 2017 the second in University of Pernambuco-Brazil and Illinois State University-USA. He is current adjunct professor at Federal University of Technology-Paraná-Brazil. He published over 40 full papers in journals and conferences. His interests includes computational intelligence, artificial neural networks, bio-inspired metaheursitics, time series forecasting, data classification and control.

ABSTRACT:

Artificial neural networks (ANNs) are computing tools inspired on the behavior of the central nervous systems of the superior organisms. The main characteristic of these structures is the ability to learn based on previous examples. They are constituted by artificial neurons, a mathematical model of the biological neuron, which are capable to transmit information from one to another. In this lecture, we present constructive aspects of the neural models, some architectures and examples of application present in the literature, mainly focused on time series forecasting.

APOIO

Departamento de Eletrônica - DAELE
UTFPR Campus Ponta Grossa
Av. Monteiro Lobato, km 4 - s/n . CEP: 84016-210
Tel: +55 (42) 3235-7042 / 3220-4825 . FAX: +55 (42) 3220-4810
Ponta Grossa - Paraná - Brasil