Zobrazeno 1 - 10
of 842
pro vyhledávání: '"Rojas Cristian"'
The sampling rate of input and output signals is known to play a critical role in the identification and control of dynamical systems. For slow-sampled continuous-time systems that do not satisfy the Nyquist-Shannon sampling condition for perfect sig
Externí odkaz:
http://arxiv.org/abs/2410.19629
Autor:
Krishnamurthy, Vikram, Rojas, Cristian
We consider word-of-mouth social learning involving $m$ Kalman filter agents that operate sequentially. The first Kalman filter receives the raw observations, while each subsequent Kalman filter receives a noisy measurement of the conditional mean of
Externí odkaz:
http://arxiv.org/abs/2410.08447
Markov parameters play a key role in system identification. There exists many algorithms where these parameters are estimated using least-squares in a first, pre-processing, step, including subspace identification and multi-step least-squares algorit
Externí odkaz:
http://arxiv.org/abs/2405.04258
Subspace identification methods (SIMs) have proven very powerful for estimating linear state-space models. To overcome the deficiencies of classical SIMs, a significant number of algorithms has appeared over the last two decades, where most of them i
Externí odkaz:
http://arxiv.org/abs/2405.04250
While subspace identification methods (SIMs) are appealing due to their simple parameterization for MIMO systems and robust numerical realizations, a comprehensive statistical analysis of SIMs remains an open problem, especially in the non-asymptotic
Externí odkaz:
http://arxiv.org/abs/2404.17331
Autor:
Rojas, Cristian, Algra-Maschio, Frank, Andrejevic, Mark, Coan, Travis, Cook, John, Li, Yuan-Fang
Misinformation about climate change poses a significant threat to societal well-being, prompting the urgent need for effective mitigation strategies. However, the rapid proliferation of online misinformation on social media platforms outpaces the abi
Externí odkaz:
http://arxiv.org/abs/2404.15673
This paper addresses a kernel-based learning problem for a network of agents locally observing a latent multidimensional, nonlinear phenomenon in a noisy environment. We propose a learning algorithm that requires only mild a priori knowledge about th
Externí odkaz:
http://arxiv.org/abs/2404.09708
Block coordinate descent is an optimization technique that is used for estimating multi-input single-output (MISO) continuous-time models, as well as single-input single output (SISO) models in additive form. Despite its widespread use in various opt
Externí odkaz:
http://arxiv.org/abs/2404.09071
Refined instrumental variable methods have been broadly used for identification of continuous-time systems in both open and closed-loop settings. However, the theoretical properties of these methods are still yet to be fully understood when operating
Externí odkaz:
http://arxiv.org/abs/2404.08955
The maximum absolute correlation between regressors, which is called mutual coherence, plays an essential role in sparse estimation. A regressor matrix whose columns are highly correlated may result from optimal input design, since there is no constr
Externí odkaz:
http://arxiv.org/abs/2402.06048