To handle possible misspecification of the structure, we propose a method named Adaptive and Robust MUlti-Task Learning (ARMUL):min Θ ∈ R d × m , Γ ∈ Ω { ∑ j = 1 m w j [ f j ( θ j ) + λ j ‖ θ j − γ j ‖ 2 ] } ....
Robust Learning Over Multitask Adaptive Networks With Wireless Communication Linksdoi:10.1109/tcsii.2018.2874090Mojtaba HajiabadiGhosheh Abed HodtaniHossein KhoshbinInstitute of Electrical and Electronics Engineers (IEEE)
In this paper, a novel adaptive multi-priority controller for redundant manipulators is proposed to accomplish the multi-task tracking when kinematic/dynamic uncertainties and unknown disturbances exist. Prioritized redundancy resolution in kinematic level is incorporated into this passivity-based control fram...
This is a project where an Adaptive Flight Control based on L1 adaptive control is designed and tested using MATLAB/Simulink [ L1 adaptive control code ] flight-controllerflight-simulatorsimulinkcontrol-systemsadaptive-controlrobustnessrobust-controll1-ac ...
‘Academic performance’, of course, is not the same as learning, although it may be used as a proxy for it, and the authors mistakenly equate the two. The use of ChatGPT may well enhance academic performance, in the same way that a line of coke may enhance sporting performance. A qui...
The global adaptive learning software market size was USD 1.61 billion in 2019 and is projected to reach USD 7.94 billion by 2027, exhibiting a CAGR of 22.1% during the forecast period.
The paper presents a simple robust algorithm for the recognition of a 2100 Hz tone with periodic phase reversal and the disabling of an echo canceller base... F Beritelli,S Casale,M Russo - International Symposium on Uncertainty Modelling & Analysis 被引量: 38发表: 1995年 An Explicit Criterio...
Independent sparsity and temporal smoothness are combined in our tracking framework to realise a robust channel selection mechanism. Thanks to the convexity of the proposed formulation, we employ an iterative optimisation technique for efficient filter learning. A deep analysis of the impact of each ...
We propose an approach based on machine learning to solve two-stage linear adaptive robust optimization (ARO) problems with binary here-and-now variables and polyhedral uncertainty sets. We encode the optimal here-and-now decisions, the worst-case scenarios associated with the optimal here-and-now...
When the task consists of driving a repeated trajectory, an adaptive look-up-table MEMory, introduced and analyzed in this paper, is however simpler to implement and results in even better control performances 展开 关键词: adaptive control feedforward learning systems manipulator dynamics nonlinear ...