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 paper we leverage the universal approximation properties of neural networks (NNs) to ...
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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Since Convolutional Neural Networks excel in recognizing objects and patterns in visual data while Recurrent Neural Networks are proficient at handling sequential data, in this research, we propose a hybrid EfficientNet-Gated Recurrent Unit (GRU) network as well as EfficientNet-B0-based transfer ...
The method is based on the observation thatamino acidsubstitutions follow a pattern within a family of homologous proteins. Therefore, if the sequence of interest has homologues within the database of known structures, this information can be used to improve predictive success, provided the homologues...
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...
Mechanics: Condition monitoring, systems modeling, and control 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, ...
Classical machine learning modeling demands considerable computing power for internal calculations and training with big data in a reasonable amount of tim
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 ...
In such cases, one needs deep convolutional and recurrent neural networks, equipped with meta-data [119]. The provided architectures take too long in training neural networks, and then one needs first-order optimizers, which are classified in SGD- and Adam-type algorithms. Such approaches do ...