P. J. Antsaklis, "Neural Networks for Control Systems", IEEE Transaction On Neural Networks, vol. 1, no. 2, pp. 242-244, June 1990.Neural networks for control systems – A survey. Automatica - Hunt, Sbarbaro, et al. - 1992 () Citation Context ... to the net and with a learning...
Neural Networks for Control Systems A Survey * This paper focuses on the promise of artificial neural networks in the realm of modelling, identification and control of nonlinear systems. The basic ideas... S Paper 被引量: 2017发表: 1992年 Neural Networks for Control Systems—A Survey This pape...
Neural networks for control systems—a survey Automatica (1992) A. Chen et al. Global robust stability of interval cellular neural networks with time-varying delays, Chaos Solitons & Fractals (2005) X.F. Liao et al. Delay-dependent exponential stability analysis of delayed neural networks: an ...
IEEE TRANSACTIONS ON SYSTEMS,MAN,AND CYBERNETICSPART C:APPLICATIONS AND REVIEWS,VOL.30,NO.4,NOVEMBER 2000 451Neural Networks for Classification:A SurveyGuoqiang Peter ZhangAbstract Classification is one of the most active research andapplication areas of neural networks.The literature is vast an...
As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article ...
TIM: An Efficient Temporal Interaction Module for Spiking Transformer [arxiv] [paper with code] [code] One-step Spiking Transformer with a Linear Complexity EC-SNN: Splitting Deep Spiking Neural Networks for Edge Devices Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers ...
论文《Backpropagation-Based Learning Techniques for Deep Spiking Neural Networks: A Survey》主要探讨了基于反向传播的深度脉冲神经网络(SNNs)的学习技术。深度SNNs因其事件驱动的计算方式,有望提高深度神经网络的延迟和能效。然而,由于脉冲事件的非可微性,训练这类网络存在挑战。在训练方法上,已有研究提出了多种策略...
In the survey we define the requirements to DF in the interests of the control of complex artificial and natural objects, consider the structure of the multilevel process of intelligent object control, identify the neural networks that can be used in the control process for data fusion. Despite...
In this paper, we have proposed a new ADP-based method to solve online the H∞ control problem for affine nonlinear discrete-time systems. The importance of the proposed method relies on simultaneous tuning the weights of the critic, action, and disturbance networks by using data generated in ...
Physics-informed neural networks use Lagrangian and Hamiltonian-based formulations of physical models as priors for different learning tasks29,30,31. They are useful tools to model partially unknown systems29 and have been also applied to control tasks30,31. Contrary to the above approaches, we ...