This paper investigates the problem of finite-time H∞ state estimation for discrete-time stochastic switched genetic regulatory networks (GRNs) with time-varying delays and exogenous disturbances. A new discrete time...
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This paper investigates the stability of linear systems with a time-varying delay. We propose a new approach to construct Lyapunuv-Krasovskii functional (LKF). Compared with other traditional approach, the proposed on...
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Real-time strategy (RTS) games have become one of the hotspots in the field of artificial intelligence research due to the large search space, long-term planning, and real-time constraint. In view of the fact that mos...
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This paper concers the H∞ control for singular systems with time-varying delay. Firstly, an augmented Lyapunov-Krasovskii functional (ALKF) is constructed by adding some integral terms which are dependent on the sing...
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This paper presents a maximum power point tracking controller for a PV solar system. The PV solar system is connected to the load through a DC-DC boost converter which is controlled by Adaptive Neuro-Fuzzy Inference S...
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To improve the accuracy of Electroencephalogram (EEG) emotion recognition, a stacking emotion classification model is proposed, in which different classification models such as XGBoost, LightGBM and Random Forest are ...
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Adaptive Dynamic Programming (ADP) with critic-actor structure is a useful way to achieve online learning control. The Gaussian-Kernel Function Adaptive Dynamic Programming (GK-ADP) algorithm does not need to preset t...
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Adaptive Dynamic Programming (ADP) with critic-actor structure is a useful way to achieve online learning control. The Gaussian-Kernel Function Adaptive Dynamic Programming (GK-ADP) algorithm does not need to preset the value function model which greatly enhances the applicability of ADP method in continuous space. However, when the complexity of the system increases in practice, the scale of sample set will increase which will induce a high computation cost. In order to speed up computation, a CUDA-Based Iterative Segmentary Gaussian-Kernel Function Adaptive Dynamic Programming algorithm( cuISGK-ADP) is presented in this paper. The algorithm uses singular value decomposition to decompose the large-scale matrix and uses CUDA with multi-threaded structure in order to enhance the performance. The comparison result illustrates that the computation burden which hinders the GK-ADP's application is reduced when the cuISGK-ADP algorithm is introduced. The proposed approach enhances the efficiency of the computation to a large extent.
An important feature of the deep learning algorithm is that the hidden layer of the neural network is more dependent on more computing resources and a larger amount of data. In this paper, we use TripletGAN method to ...
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