The most cited Transactions On Cybernetics paper in 2021-2022 so far

Hongyi Li, Ying Wu and Mou Chen, "Adaptive Fault-Tolerant Tracking Control for Discrete-Time Multiagent Systems via Reinforcement Learning Algorithm," in IEEE Transactions on Cybernetics, vol. 51, no. 3, pp. 1163-1174, March 2021, doi: 10.1109/TCYB.2020.2982168. | IEEE Journals & Magazine | IEEE Xplore

Abstract: This article investigates the adaptive fault-tolerant tracking control problem for a class of discrete-time multiagent systems via a reinforcement learning algorithm. The action neural networks (NNs) are used to approximate unknown and desired control input signals, and the critic NNs are employed to estimate the cost function in the design procedure. Furthermore, the direct adaptive optimal controllers are designed by combining the backstepping technique with the reinforcement learning algorithm. Comparing the existing reinforcement learning algorithm, the computational burden can be effectively reduced by using the method of less learning parameters. The adaptive auxiliary signals are established to compensate for the influence of the dead zones and actuator faults on the control performance. Based on the Lyapunov stability theory, it is proved that all signals of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, some simulation results are presented to illustrate the effectiveness of the proposed approach.

Xiaoqing Gu; Weiwei Cai; Ming Gao; Yizhang Jiang; Xin Ning; Pengjiang Qian, “Multi-Source Domain Transfer Discriminative Dictionary Learning Modeling for Electroencephalogram-Based Emotion Recognition" in IEEE Transactions on Computational Social Systems ( Volume: 9, Issue: 6, December 2022)

Abstract: Cognitive computing is dedicated to researching a computing principle and method that can simulate the intelligence ability of human brain. Human emotion is the basic component of human cognitive activities. Electroencephalogram (EEG) computer signals obtained from a brain computer interface are difficult to conceal, and using machine learning methods to analyze EEG emotion is a hot topic in artificial intelligence. However, the EEG signal is non-stationary, making it difficult to select sufficient data from the same person to train a classifier for a subject. To promote the performance of emotion recognition methods, a multi-source domain transfer discriminative dictionary learning modeling (MDTDDL) is proposed in this study. The method integrates transfer learning and dictionary learning in a learning model, including the concepts of subspace learning, manifold smoothness, margin-based discriminant embedding, and large margin. The domain-specific transformation matrix projects EEG signals from various domains into the transfer subspace. The domain-invariant dictionary can find potential connections between multiple source domains and target domain. The manifold smoothness and margin-based discriminant embedding term further improve the model's learning ability. The alternating optimization technique is used in model solving to efficiently compute model parameters. Experiments on the SEED and DEAP datasets demonstrate the effectiveness of MDTDDL.

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