"Soft computing" is computing in the presence of uncertainty,
qualitative informations, partial/contradictory data -
in other words, tackling real problems with an approach modeled after
Techniques for this task are for instance:
- Fuzzy Systems
- Neural Networks
- Evolutionary Computation
- Machine Learning
Our research activity is focused on the study of these techniques,
from the points of view of theory and implementation, and on the application
to problems from many areas (including, but not limited to,
industrial automation, environmental monitoring, intelligent transportation
systems, medical diagnosis).
Current research issues involve the study of ensemble learning machines
and the development of a class of "partially possibilistic"
classification and clustering methods.