Dr. Marley Maria B.R. Vellasco            
Professor, ICA Coordinator
ICA: Research Center of Applied Computational Intelligence
Dept. Engenharia Elétrica,
Pontifícia Universidade Católica do Rio de Janeiro - PUC-Rio
R. Marquês S. Vicente 225 - Gávea
CXP 38063                                                    
CEP 22453-900 - Rio de Janeiro - RJ
BRASIL    
Phone: +55 21 3527 1630
Email: marley@ele.puc-rio.br
http://www.ica.ele.puc-rio.br

DVP term expires December 2009

Marley Maria Bernardes Rebuzzi Vellasco received the BSc and MSc degrees in Electrical Engineering from the Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil, in 1984 and 1987, respectively, and the PhD degree in Computer Science from the University College London (UCL) in 1992.

Dr. Vellasco is currently an Assistant Professor at the Electrical Engineering Department of PUC-Rio and heads the Applied Computational Intelligence Laboratory (ICA) of PUC-Rio. She is the author of 29 papers in professional journals and more than 200 papers in conference proceedings in the area of soft computing. She has published two books and 9 book chapters. She supervised 35 MSc Dissertations and 16 Phd Thesis. Between 1991 and 2006 she participated in 30 research projects, coordinating 10 of them. Currently she participates in 12 research projects, coordinating 5 of them. Her research interests include Neural Networks, Fuzzy Logic, Neuro-Fuzzy Systems and Evolutionary Computation for decision support systems, pattern classification, time-series forecasting, control, optimization, Knowledge Discovery Databases and Data Mining.

Hierarchical Neuro-Fuzzy Systems
This conference presents a class of neuro-fuzzy models, called Hierarchical Neuro-Fuzzy BSP Systems (HNFB). These models employ recursive partition of the input space (Binary Space Partitioning and Politree partitioning) and has been developed in order to bypass the traditional drawbacks of neuro-fuzzy systems: the reduced number of allowed inputs and the poor capacity to create their own structure. The talk briefly introduces the HNFB model based on supervised learning algorithm. Then it details the RL_HNFB and RL_HNFP models, which is a hierarchical neuro-fuzzy system with reinforcement learning process. The RL_HNFB and RL_HNFP models were evaluated in benchmark applications, yielding good performance when compared with different reinforcement learning models.