Methods and tools based on interval analysis

Ferrer Mallorqui, Ines (Participant)
Calm Puig, Remei (Participant)
Figueras Coma, Albert (Participant)
Vehi Casellas, Jose (Participant)
Luo Ren, Ningsu (Participant)
Armengol Llobet, Joaquim (Participant)
Beneyto Tantiña, Aleix (Participant)
Contreras Fernandez-Davila, Ivan (Participant)
Luque Urrutia, Jesus Antonio (Participant)
Estremera Toledo, Ernesto (Participant)
Cabrera Tejera, Alvis (Participant)
Mujahid, Omer (Participant)
Najib Ur Rehman, Syed (Participant)
Ibrahim, Muhammad (Participant)
Casadesus Anglada, Sara (Participant)
Capilla Manzano, Marc (Participant)
Basora Reixach, Nil (Participant)
Sundas, Amina (Participant)
Soler Boada, Júlia (Participant)
Noguer i Torres, Josep (Participant)
Liu, Chengli (Participant)
Rodriguez Sarmiento, Xiomara (Participant)

Modeling and Intelligent Control Engineering (MiceLab) Research Group

Research line: Impact

Description of impact

The imprecision and the uncertainty of complex systems can be represented in many cases by interval models. the goal of this research line is to develop methods and tools based on interval analysis to deal with this kind of system. Our research group, have developed the following tools using interval analysis - IvalDv: Modal Interval arithmetic computations - FSTAR: Inner and outer approximations of the *-semantic extension of a continuous function. - QSI: Quantified Set Inversion - MINIMAX: optimization algorithms - SQUALTRACK: fault detection Interval analysis has been applied in several fields such as modeling, simulation, fault detection, set inversion, optimization problems, robust control analysis and design, computer graphics, biomedicine, etc.