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  1.  7
    On the Observability of Leader-Based Multiagent Systems with Fixed Topology.Bo Liu, Ningsheng Xu, Housheng Su, Licheng Wu & Jiahui Bai - 2019 - Complexity 2019:1-10.
    This paper investigates the observability of first-order, second-order, and high-order leader-based multiagent systems with fixed topology, respectively. Some new algebraic and graphical characterizations of the observability for the first-order MASs are established based on agreement protocols. Moreover, under the same leader-following framework with the predefined topology and leader assignment, the observability conditions for systems of double-integrator and high-integrator agents are also obtained. Finally, the effectiveness of the theoretical results is verified by numerical examples and simulations.
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  2.  8
    A New Perspective to Algebraic Characterization on Controllability of Multiagent Systems.Bo Liu, Housheng Su, Licheng Wu & Shengchao He - 2020 - Complexity 2020:1-12.
    Recently, algebraic characterization of multiagent controllability through its topology has been widely concerned by the systems and control community. The controllability of leader-follower networked multiagent systems under the framework of generic linear dynamics is firstly discussed via λ-matrix. Some new algebra-theoretic necessary and/or sufficient conditions of the controllability for generic linear multiagent systems are established. Moreover, the controllable conditions for multiagent networks with special topological graphs through λ-matrix are presented.
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  3.  7
    Hybrid Online and Offline Reinforcement Learning for Tibetan Jiu Chess.Xiali Li, Zhengyu Lv, Licheng Wu, Yue Zhao & Xiaona Xu - 2020 - Complexity 2020:1-11.
    In this study, hybrid state-action-reward-state-action and Q-learning algorithms are applied to different stages of an upper confidence bound applied to tree search for Tibetan Jiu chess. Q-learning is also used to update all the nodes on the search path when each game ends. A learning strategy that uses SARSAλ and Q-learning algorithms combining domain knowledge for a feedback function for layout and battle stages is proposed. An improved deep neural network based on ResNet18 is used for self-play training. Experimental results (...)
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    On the Group Controllability of Leader-Based Continuous-Time Multiagent Systems.Bo Liu, Licheng Wu, Rong Li, Housheng Su & Yue Han - 2020 - Complexity 2020:1-11.
    The group controllability of continuous-time multiagent systems with multiple leaders is considered in this paper, where the entire group is compartmentalized into a few subgroups. The group controllability concept of continuous-time MASs with multiple leaders is put forward, and the group controllability criteria are obtained for switching and fixed topologies, respectively. Finally, the numerical simulations are given to prove the validity of the theoretical results.
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    Construction and Analysis of Emotion Computing Model Based on LSTM.Huiping Jiang, Rui Jiao, Zequn Wang, Ting Zhang & Licheng Wu - 2021 - Complexity 2021:1-12.
    The electroencephalogram is the most common method used to study emotions and capture electrical brain activity changes. Long short-term memory processes the temporal characteristics of data and is mostly used for emotional text and speech recognition. Since an EEG involves a time series signal, this article mainly studied the introduction of LSTM for emotional EEG recognition. First, an ALL-LSTM model with a four-layered LSTM network was established in which the average accuracy rate for emotional classification reached 86.48%. Second, four EEG (...)
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