Zobrazeno 1 - 10
of 2 562
pro vyhledávání: '"A. Bonassi"'
In this report we investigate the use of the Tustin neural network architecture (Tustin-Net) for the identification of a physical rotary inverse pendulum. This physics-based architecture is of particular interest as it builds on the known relationshi
Externí odkaz:
http://arxiv.org/abs/2408.12266
Recently, several direct Data-Driven Predictive Control (DDPC) methods have been proposed, advocating the possibility of designing predictive controllers from historical input-output trajectories without the need to identify a model. In this work, we
Externí odkaz:
http://arxiv.org/abs/2403.05860
This paper explores the use of Control Affine Neural Nonlinear AutoRegressive eXogenous (CA-NNARX) models for nonlinear system identification and model-based control design. The idea behind this architecture is to match the known control-affine struc
Externí odkaz:
http://arxiv.org/abs/2402.05607
The goal of this paper is to provide a system identification-friendly introduction to the Structured State-space Models (SSMs). These models have become recently popular in the machine learning community since, owing to their parallelizability, they
Externí odkaz:
http://arxiv.org/abs/2312.06211
Autor:
Tiziana Lencioni, Mario Meloni, Thomas Bowman, Ilaria Carpinella, Valerio Gower, Susanna Mezzarobba, Carola Cosentino, Gaia Bonassi, Martina Putzolu, Maurizio Ferrarin, Laura Avanzino, Elisa Pelosin
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Freezing of gait (FOG) in Parkinson’s disease (PD) can be triggered by sensomotor, cognitive or limbic factors. The limbic system’s impact on FOG is attributed to elevated limbic load, characterized by aversive stimuli, potentially deple
Externí odkaz:
https://doaj.org/article/a5bb9d84b749406d8751476f3a553f98
Publikováno v:
Automatica 159 (2024) 111381
This brief addresses the design of a Nonlinear Model Predictive Control (NMPC) strategy for exponentially incremental Input-to-State Stable (ISS) systems. In particular, a novel formulation is devised, which does not necessitate the onerous computati
Externí odkaz:
http://arxiv.org/abs/2309.16428
The aim of this work is to investigate the use of Incrementally Input-to-State Stable ($\delta$ISS) deep Long Short Term Memory networks (LSTMs) for the identification of nonlinear dynamical systems. We show that suitable sufficient conditions on the
Externí odkaz:
http://arxiv.org/abs/2304.02975
Autor:
Gláucia Maria Moraes de Oliveira, Maria Cristina Costa de Almeida, Carolina María Artucio Arcelus, Larissa Espíndola Neto, Maria Alayde Mendonça Rivera, Agnaldo Lopes da Silva-Filho, Celi Marques-Santos, César Eduardo Fernandes, Carlos Japhet da Matta Albuquerque, Claudia Maria Vilas Freire, Maria Cristina de Oliveira Izar, Maria Elizabeth Navegantes Caetano Costa, Marildes Luiza de Castro, Viviana de Mello Guzzo Lemke, Alexandre Jorge Gomes de Lucena, Andréa Araujo Brandão, Ariane Vieira Scarlatelli Macedo, Carisi Anne Polanczyk, Carla Janice Baister Lantieri, Eliana Petri Nahas, Elizabeth Regina Giunco Alexandre, Erika Maria Gonçalves Campana, Érika Olivier Vilela Bragança, Fernanda Marciano Consolim Colombo, Imara Correia de Queiroz Barbosa, Ivan Romero Rivera, Jaime Kulak, Lidia Ana Zytynski Moura, Luciano de Mello Pompei, Luiz Francisco Cintra Baccaro, Marcia Melo Barbosa, Marcio Alexandre Hipólito Rodrigues, Marco Aurelio Albernaz, Maria Sotera Paniagua de Decoud, Maria Sanali Moura de Oliveira Paiva, Martha Beatriz Sanchez-Zambrano, Milena dos Santos Barros Campos, Monica Acevedo, Monica Susana Ramirez, Olga Ferreira de Souza, Orlando Otávio de Medeiros, Regina Coeli Marques de Carvalho, Rogerio Bonassi Machado, Sheyla Cristina Tonheiro Ferro da Silva, Thais de Carvalho Vieira Rodrigues, Walkiria Samuel Avila, Lucia Helena Simões da Costa-Paiva, Maria Celeste Osorio Wender
Publikováno v:
Revista Brasileira de Ginecologia e Obstetrícia, Vol 46 (2024)
Externí odkaz:
https://doaj.org/article/b1fe82d65ed741289819831593ad3f72
This paper presents a robust Model Predictive Control (MPC) scheme that provides offset-free setpoint tracking for systems described by Neural Nonlinear AutoRegressive eXogenous (NNARX) models. The NNARX model learns the dynamics of the plant from in
Externí odkaz:
http://arxiv.org/abs/2210.06801
Autor:
Alessandro Botta, Elisa Pelosin, Giovanna Lagravinese, Roberta Marchese, Francesca Di Biasio, Gaia Bonassi, Sara Terranova, Elisa Ravizzotti, Martina Putzolu, Susanna Mezzarobba, Carola Cosentino, Alessio Avenanti, Laura Avanzino
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-9 (2024)
Abstract Valence (positive and negative) and content (embodied vs non-embodied) characteristics of visual stimuli have been shown to influence motor readiness, as tested with response time paradigms. Both embodiment and emotional processing are affec
Externí odkaz:
https://doaj.org/article/0a21b1a02afb4b8489df1976dbe9c167