Stefania Tomasiello
Papers
1
Total Citations
6
H-Index
1
About
Stefania Tomasiello is a researcher whose work bridges computational intelligence, control theory, and applied mathematics. Her primary research areas include functional networks, iterative learning control, and soft computing methods for complex systems. A key contribution is her development of fitted Q-iteration using functional networks, a novel approach that enhances reinforcement learning for control problems. This work, detailed in her 2016 paper of the same name, has garnered 6 citations and demonstrates her ability to integrate machine learning with dynamic system optimization. Tomasiello’s research is distinguished by its focus on practical, data-driven solutions for nonlinear and uncertain environments, often employing functional networks to model and solve challenging engineering tasks. Her achievements include advancing the theoretical foundations of functional networks and applying them to real-world control scenarios, making her work valuable for students and researchers in computational intelligence and automation. Through her innovative methodologies, Tomasiello contributes to more efficient and adaptive control systems, with her cited work serving as a reference point for those exploring reinforcement learning in continuous state-action spaces.
Research Focus
Key Achievements
Top Papers
- 1Fitted Q-iteration by Functional Networks for control problems6 citations · 2016