Zobrazeno 1 - 10
of 164
pro vyhledávání: '"YANG Zewen"'
Artificial Intelligence is rapidly advancing and radically impacting everyday life, driven by the increasing availability of computing power. Despite this trend, the adoption of AI in real-world healthcare is still limited. One of the main reasons is
Externí odkaz:
http://arxiv.org/abs/2410.18725
The growing AI field faces trust, transparency, fairness, and discrimination challenges. Despite the need for new regulations, there is a mismatch between regulatory science and AI, preventing a consistent framework. A five-layer nested model for AI
Externí odkaz:
http://arxiv.org/abs/2407.16888
Publikováno v:
Zhongguo Jianchuan Yanjiu, Vol 13, Iss 6, Pp 9-18 (2018)
With the development of artificial intelligence technology,the missions to be performed by unmanned systems are more and more complicated,and the systems are required to complete given missions independently in an unknown environment. In order to sol
Externí odkaz:
https://doaj.org/article/edb948155b344ade8a7bcd9cd3c6a4a0
Safe control for dynamical systems is critical, yet the presence of unknown dynamics poses significant challenges. In this paper, we present a learning-based control approach for tracking control of a class of high-order systems, operating under the
Externí odkaz:
http://arxiv.org/abs/2405.00822
Consensus control in multi-agent systems has received significant attention and practical implementation across various domains. However, managing consensus control under unknown dynamics remains a significant challenge for control design due to syst
Externí odkaz:
http://arxiv.org/abs/2402.03174
Autor:
Yang, Zewen, Dong, Songbo, Lederer, Armin, Dai, Xiaobing, Chen, Siyu, Sosnowski, Stefan, Hattab, Georges, Hirche, Sandra
This work presents an innovative learning-based approach to tackle the tracking control problem of Euler-Lagrange multi-agent systems with partially unknown dynamics operating under switching communication topologies. The approach leverages a correla
Externí odkaz:
http://arxiv.org/abs/2402.03048
This paper introduces an innovative approach to enhance distributed cooperative learning using Gaussian process (GP) regression in multi-agent systems (MASs). The key contribution of this work is the development of an elective learning algorithm, nam
Externí odkaz:
http://arxiv.org/abs/2402.03014
The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs). To infer the unknown part of the
Externí odkaz:
http://arxiv.org/abs/2307.13945
When the dynamics of systems are unknown, supervised machine learning techniques are commonly employed to infer models from data. Gaussian process (GP) regression is a particularly popular learning method for this purpose due to the existence of pred
Externí odkaz:
http://arxiv.org/abs/2305.08169
In the realm of the cooperative control of multi-agent systems (MASs) with unknown dynamics, Gaussian process (GP) regression is widely used to infer the uncertainties due to its modeling flexibility of nonlinear functions and the existence of a theo
Externí odkaz:
http://arxiv.org/abs/2304.05138