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...
(2021a), a data-driven performance-prescribed RL control method was proposed to deal with the complicated marine environment by designing a state transformation module. Zhang et al. (2020) presented a model-reference RL method with classic control for uncertain USVs, in which control policy can...
Recent improvements in hardware and data collection have lowered the barrier to practical neural control. Most of the current contributions to the field have focus on model-based control, however, models of neural systems are quite complex and difficult
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 ...
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 ...
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...
IMU and so on. Regarding the control layer, some of most used control methods are the PID control method [6,24], the Model Predictive Control algorithm [25], the Fuzzy Control method [9,21], the Model-Reference Adaptive method [4,46], the Fractional Order control method [52], the Pur...
In the following sections of this chapter, any reference to the convolution operation will mostly refer to the 2D discrete case. The extension to the 3D case, which is often encountered in medical imaging, is straightforward. 4.2 Properties of the Convolution Operation In the case of a discrete...
The “deep” in deep learning is not a reference to any kind of deeper understanding that the approach achieves, rather, it stands for this notion of effective layers of representations. Modern deep learning often involves tens or even hundreds of successive layers of representations — and from...
In current in situ X-ray diffraction (XRD) techniques, data generation surpasses human analytical capabilities, potentially leading to the loss of insights. Automated techniques require human intervention, and lack the performance and adaptability requir