提出了一种新的神经自适应结构:基于深度神经网络的模型参考自适应控制(DMRAC)。我们的体系结构利用深度神经网络表示的能力来建模显著的非线性,同时将其与表征基于MRAC的控制器的有界性保证相结合。我们通过仿真和分析证明,DMRAC可以包含以前研究的基于学习的MRAC方法,如并发学习和GP-MRAC。这使得DMRAC具有强大的体系结构...
The self-tuning regulator control and model reference adaptive control are two classic adaptive control methods. However, they were initially proposed to address disturbances in linear systems (Astrom and Wittenmark, 1994). The adaptive control problem of nonlinear systems is much more complicated than...
computationally efficient and scalable. The key component is to respect the extensive and symmetry-invariant properties of a potential energy model by assigning a local reference frame and a local environment to each atom. Each environment contains a finite number of atoms, whose local coordinates are...
biped robots; single rigid body; model predictive control; deep reinforcement learning1. Introduction In this paper, deep reinforcement learning (DRL) is used to predict the disturbances of the swinging leg to the single rigid body (SRB) model, and the SRB-based model predictive control (MPC) ...
Search for the usage of a specific API in the API reference manual, which organizes all DGL APIs by their namespace. All the learning materials are available at our documentation site. If you are new to deep learning in general, check out the open source book Dive into Deep Learning. Comm...
The convergence performance of RSPSO algorithm was evaluated with 6 DNN model architecture optimisation. To assess the optimisation ability of RSPSO, the performance of the reference GA-enhanced DNN model is studied. 4.1.1. RSPSO parameters selection As shown in Eq. (14), RSPSO parameters ...
Based on it, an adaptive minimum variance control scheme is then proposed to restore the spectral features of the Vim's LFPs to reference values, i.e., as in subjects not affected by movement disorders. Results indicate good performances in tracking the reference spectral features through ...
To verify the effectiveness of the manipulator control using the trained model, the manipulator is controlled to perform continuous motion using the reinforcement learning model based on the given expected trajectory. As shown in Fig. 6, the path obtained based on reinforcement learning is almost ide...
aligning specific security threats with the corresponding components of the Cisco IoT reference model to provide a holistic understanding of the potential vulnerabilities in the IoT ecosystem. The progress in machine learning and deep learning has opened up new possibilities for creating potent techniques...
Simultaneously compensating a large number of aberration types also enables the capacity of DL-AO in autonomous control of the deformable mirror in response to random and dynamic aberration changes. Fig. 1: Deep learning-driven adaptive optics for single-molecule localization microscopy. Upon the ...