Data-enabled predictive control (DeePC) for linear systems utilizes data matrices of recorded trajectories to directly predict new system trajectories, which is very appealing for real-life applications. In this
data in two monkeys performing three-dimensional reach and grasp tasks, PSID revealed that the behaviorally relevant dynamics are significantly lower-dimensional than otherwise implied. Moreover, PSID discovered distinct rotational dynamics that were more predictive of behavior. Furthermore, PSID more ...
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We use “Metamodel” terminology (Belyaev et al., 2016) to describe a purely data-driven 3D reservoir model approximating the solution given by a base model (e.g. FDHS), and we provide a detailed definition in Section 3. We concentrated our attention on modelling so-called development ...
Robotics: Forklift robots, manipulator controllers, trajectory control, and vision systems Telecommunications: ATM network control, automated information services, customer payment processing systems, data compression, equalizers, fault management, handwriting recognition, network design, management, routing and ...
PGSLM: Edge-enabled probabilistic graph structure learning model for traffic forecasting in Internet of vehicles[J]. China Communications, 2023, 20(4): 270-286. Link Xu M, Qiu T Z, Fang J, et al. Signal-control Refined Dynamic Traffic Graph Model for Movement-based Arterial Network Traffic...
Classical machine learning modeling demands considerable computing power for internal calculations and training with big data in a reasonable amount of tim
learning framework resulting in an increased precision and universality. Broader acceptance and applicability of deep learning would require inclusion of ground-truth datasets and should feature integration of mechanisms for fusion of data from multiple provenances; thus making the models robust and field...
Our approach enabled the construction of functioning ENNs with above-chance task performance without optimizing for behavior; instead, we were able to derive parameters from empirical neural data alone. Theoretically, the results presented here are consistent with the goals of the Dynamic Causal ...
Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neural