Zobrazeno 1 - 10
of 959
pro vyhledávání: '"Chiuso A"'
Direct data-driven design methods for the linear quadratic regulator (LQR) mainly use offline or episodic data batches, and their online adaptation has been acknowledged as an open problem. In this paper, we propose a direct adaptive method to learn
Externí odkaz:
http://arxiv.org/abs/2401.14871
Model Predictive Control (MPC) is a powerful method for complex system regulation, but its reliance on an accurate model poses many limitations in real-world applications. Data-driven predictive control (DDPC) aims at overcoming this limitation, by r
Externí odkaz:
http://arxiv.org/abs/2312.14788
Autor:
Casti, Umberto, Baggio, Giacomo, Benozzo, Danilo, Zampieri, Sandro, Bertoldo, Alessandra, Chiuso, Alessandro
In this paper, we consider stable stochastic linear systems modeling whole-brain resting-state dynamics. We parametrize the state matrix of the system (effective connectivity) in terms of its steady-state covariance matrix (functional connectivity) a
Externí odkaz:
http://arxiv.org/abs/2310.07262
Autor:
Fazzi, Antonio, Chiuso, Alessandro
In this paper, we study connections between the classical model-based approach to nonlinear system theory, where systems are represented by equations, and the nonlinear behavioral approach, where systems are defined as sets of trajectories. In partic
Externí odkaz:
http://arxiv.org/abs/2304.02930
Publikováno v:
62nd IEEE Conference on Decision and Control, Dec. 13-15, 2023, Singapore
Model predictive control (MPC) is a control strategy widely used in industrial applications. However, its implementation typically requires a mathematical model of the system being controlled, which can be a time-consuming and expensive task. Data-dr
Externí odkaz:
http://arxiv.org/abs/2304.00263
Publikováno v:
IFAC-PapersOnLine, Volume 56, Issue 2, 2023, Pages 10083-10088, ISSN 2405-8963
Data-Driven Predictive Control (DDPC) has been recently proposed as an effective alternative to traditional Model Predictive Control (MPC), in that the same constrained optimization problem can be addressed without the need to explicitly identify a f
Externí odkaz:
http://arxiv.org/abs/2211.10321
Autor:
Danilo Benozzo, Giacomo Baggio, Giorgia Baron, Alessandro Chiuso, Sandro Zampieri, Alessandra Bertoldo
Publikováno v:
Harvard Data Science Review, Vol 8, Iss 3 (2024)
Externí odkaz:
https://doaj.org/article/a201be7830e343119ad97644ffacb3eb
Autor:
Rinaldi, Laura, Senatore, Emanuela, Feliciello, Stella, Chiuso, Francesco, Insabato, Luigi, Feliciello, Antonio
Publikováno v:
In BBA - Reviews on Cancer February 2025 1880(1)
Autor:
Danilo Benozzo, Giorgia Baron, Ludovico Coletta, Alessandro Chiuso, Alessandro Gozzi, Alessandra Bertoldo
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract Exploring how the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the major goals of modern neuroscience. At the macroscale level, no one-to-one correspondence between structura
Externí odkaz:
https://doaj.org/article/669e9d81c8f54a68b7f0aff3473292ed
Downregulation of praja2 restrains endocytosis and boosts tyrosine kinase receptors in kidney cancer
Autor:
Laura Rinaldi, Francesco Chiuso, Emanuela Senatore, Domenica Borzacchiello, Luca Lignitto, Rosa Iannucci, Rossella Delle Donne, Mariano Fuggi, Carla Reale, Filomena Russo, Nicola Antonino Russo, Giorgio Giurato, Francesca Rizzo, Assunta Sellitto, Michele Santangelo, Davide De Biase, Orlando Paciello, Chiara D’Ambrosio, Stefano Amente, Corrado Garbi, Emiliano Dalla, Andrea Scaloni, Alessandro Weisz, Concetta Ambrosino, Luigi Insabato, Antonio Feliciello
Publikováno v:
Communications Biology, Vol 7, Iss 1, Pp 1-15 (2024)
Abstract Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer in the adult population. Late diagnosis, resistance to therapeutics and recurrence of metastatic lesions account for the highest mortality rate among kidney cancer pati
Externí odkaz:
https://doaj.org/article/9d16140add05426185224d8f68ffc708