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Bipartite synchronization of Lur?e network with signed graphs based on intermittent control

ISA TRANSACTIONS 2023年 135卷 290-298页

作者： Yang, Jinyue Huang, Junjian He, Xing Yang, Wenqiang Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

In this paper, the bipartite synchronization of signed Lur'e network is studied under intermittent control, where the communication relationship of these adjacent nodes in the network can be either cooperative or competitive. Assuming that the network is structurally balanced, bipartite synchronization can be reached with some conditions and coordinate transform criterion. Then, Based on Lyapunov stability theory, some important norms are established. Ultimately, the simulation results can illustrate validness of theoretical analysis. (c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Bipartite synchronization Lur?e network Signed graphs Intermittent control Leader-following synchronization

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Event-Triggered Containment Control for nonlinear Multiagent Systems via Reinforcement Learning

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IEEE TRANSACTIONS ON circuits AND SYSTEMS II-EXPRESS BRIEFS 2023年第8期70卷 2904-2908页

作者： Wang, Zichen Wang, Xin Zhao, Chen Southwest Univ Coll Westa Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

Herein, we concern with the problem of event-triggered containment optimal control for a class of nonlinear multiagent discrete-time systems (MADSs). Compared to the previous containment control strategy for linear MADSs, a novel containment control strategy for nonlinear MADSs via the backstepping technique is introduced. Utilizing the classic reinforcement learning (RL) approach, we implement the ${n}$ -step backstepping structure with actor-critic neural networks (NNs). In addition, instead of a time-triggered scheme, the event-triggered scheme (ETS) is employed for saving computations. Finally, some simulation results are presented to verify the performance of our control strategy.

关键词： Containment control event-triggered reinforcement learning nonlinear multiagent systems

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Nonconvex low-rank tensor approximation with graph and consistent regularizations for multi-view subspace learning

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NEURAL NETWORKS 2023年第1期161卷 638-658页

作者： Pan, Baicheng Li, Chuandong Che, Hangjun Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

Multi-view clustering is widely used to improve clustering performance. Recently, the subspace clustering tensor learning method based on Markov chain is a crucial branch of multi-view clus-tering. Tensor learning is commonly used to apply tensor low-rank approximation to represent the relationships between data samples. However, most of the current tensor learning methods have the following shortcomings: the information of the local graph is not taken into account, the relationships between different views are not shown, and the existing tensor low-rank representation takes a biased tensor rank function for estimation. Therefore, a nonconvex low-rank tensor approximation with graph and consistent regularizations (NLRTGC) model is proposed for multi-view subspace learning. NLRTGC retains the local manifold information through graph regularization, and adopts a consistent regularization between multi-views to keep the diagonal block structure of representation matrices. Furthermore, a nonnegative nonconvex low-rank tensor kernel function is used to replace the existing classical tensor nuclear norm via tensor-singular value decomposition (t-SVD), so as to reduce the deviation from rank. Then, an alternating direction method of multipliers (ADMM) which makes the objective function monotonically non-increasing is proposed to solve NLRTGC. Finally, the effectiveness and superiority of the NLRTGC are shown through abundant comparative experiments with various state-of-the-art algorithms on noisy datasets and real world datasets.(c) 2023 Elsevier Ltd. All rights reserved.

关键词： Multi -view clustering Subspace clustering Spectral clustering Nonconvex low -rank tensor approximation

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Neural-Network-Based Adaptive Consensus Control for nonlinear Multiagent Systems Subject to Time Delays and Unknown Disturbance

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NEURAL proCESSING LETTERS 2023年第9期55卷 11885-11904页

作者： Wen, Ruolan Wang, Xin Xu, Rui Pang, Ning Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400044 Peoples R China

The current investigation aims at the adaptive consensus issue for a class of nonlinear multiagent systems with time delays and unknown disturbances under a directed graph topology. A novel designed disturbance observer eliminates the impact on the consensus induced by unknown external disturbances. Furthermore, the Lyapunov-Krasovskii functional method is applied to tackle the time delays in the design process. With the approximation capability of neural networks, the adaptive controller is developed by the backstepping technique, which can alleviate the burden of computing and make the consensus tracking error converge to a small compact region. The control scheme proposed in this paper enables the nonlinear multiagent systems to achieve consensus output. Meanwhile, a numerical simulation example is performed to verify the effectiveness of the proposed scheme.

关键词： nonlinear multiagent systems Consensus issue Neural network Disturbance obsever Time delays

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Neural Network-Based Adaptive Containment Control Algorithms Design for nonlinear Multiagent Systems with Switching Topologies

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COGNITIVE COMPUTATION 2023年第1期15卷 90-102页

作者： Huang, Jie Wang, Xin Xu, Rui Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400044 Peoples R China

This paper discusses the containment control problem of nonlinear multiagent systems with unknown disturbance and switching topology. The novel model of nonlinear multiagent systems is given and the dynamic functions of this model are considered to be continuous. Utilizing the approximation capability of the neural networks to estimate the unknown external disturbance and unknown nonlinear functions in the dynamic functions of nonlinear multiagent systems, which designs disturbance observer and containment controller. The containment control problem can be addressed with the proposed algorithm, applying the Lyapunov functions, graph theory, and inequality techniques. The nonlinear multiagent systems are cooperatively semi-globally uniformly ultimately bounded and at the end of this paper, a simulation example is given to illustrate the effectiveness of the approach proposed.

关键词： Neural network Multiagent systems Switching topologies Adaptive containment control

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Time-delayed reservoir computing using mutually coupled multimode semiconductor laser for high-speed image recognition

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OPTICS AND LASER TECHNOLOGY 2025年 187卷

作者： Li, Zelin Xie, Yiyuan Xu, Fang Ye, Yichen Jiang, Xiao Su, Ye Li, Lili Chen, Zhuang Tang, Yuhan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Univ Chongqing Key Lab Networks & Cloud Comp Secur Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

Reservoir computing (RC), especially time-delayed RC, which derives from recurrent neural network-based models, has the advantages of being easy to implement at the physical level and having a low training cost. Nowadays, time-delayed RC is being used to handle more complex tasks such as image processing. However, for time-delayed RC, the more complex the tasks requires more virtual nodes, resulting in longer delay lines being employed. This leads to a reduction in the RC information processing rate. To overcome this drawback, multimode semiconductor lasers with multiple modes offer a solution. In our work, we use the mutually coupled multimode semiconductor lasers as the physical nodes to construct a time-delayed RC system. Finally, two MC-MSLs were used, each with four modes. It greatly increased the number of virtual nodes at the same information processing rate. By training the RC with input extracted representative features, we have successfully realized parallel processing of the image recognition task and achieved 99.20% and 86.10% accuracies on MNIST and Fashion-MNIST datasets. Given the expansion of multimode semiconductor lasers in longitudinal mode, MC-MSLs RC is expected to enable high-speed processing of more complex tasks.

关键词： Reservoir computing Multimode semiconductor laser Image recognition Parallel high-speed information processing

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Observer-Based Event-Triggered Optimal Control for nonlinear Multiagent Systems With Input Delay via Reinforcement Learning Strategy

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第3期9卷 2398-2409页

作者： Wang, Xin Liao, Yujie Tan, Lihua Zhang, Wei Li, Huaqing Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Chongqing Jiaotong Univ Sch Math & Stat Chongqing 400074 Peoples R China

Ever since the reinforcement learning method has been proposed, the optimal control problem for multiagent systems has been intensively explored in light of the energy consumption growth. However, most of the consequences only focused on ideal communication cases without input delay. Distinguished from the existing optimal control mechanisms, the paper concentrates on the event-triggered optimal tracking control problem for nonlinear multi-agent systems with unmeasurable states and input delay. A state observer taking into account the universal approximation capability of neural networks is proposed to tackle the unmeasurable state variables which are often encountered in practical engineering scenarios. Subsequently, the optimal backstepping control mechanism is developed according to the reinforcement learning method, where the actor-critic networks are utilized to obtain the modified control parameters. Finally, an event-triggered strategy, which relies both on the latest sampled state and a non-negative threshold, is presented to relieve the communication frequency of the control signals with guaranteed performance requirements. The illustrated simulations are presented to confirm the feasibility of the proposed optimal tracking control algorithms.

关键词： Multiagent systems reinforcement learning optimal backstepping control input delay

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Stability Analysis of T-S Fuzzy State-Dependent Impulsive nonlinear Systems Based on New Improved Functional

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INTERNATIONAL JOURNAL OF ROBUST AND nonlinear CONTROL 2025年第0期

作者： Chang, Fei Li, Chuandong Zhou, Yinghua Deng, Hao Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing Peoples R China Chongqing Univ Sci & Technol Sch Intelligent Technol & Engn Chongqing Peoples R China

In this paper, the authors propose an extended functional-based approach and develop two new improved functionals to investigate the stability of T-S fuzzy nonlinear systems with state-dependent impulsive inputs (TSFSSI). Firstly, the conversion of nonlinear systems characterized by state-dependent impulsive inputs is accomplished by employing the T-S fuzzy model and B-equivalence method, resulting in T-S fuzzy nonlinear systems with fixed-time impulsive inputs (TSFSFI). Secondly, two novel improved functionals that rely on bilateral looped functional (BLF), Lyapunov functions, and additional new functional elements are proposed. In contrast to BLF, the improved functionals not only alleviate the positivity constraints on Lyapunov functions, thereby reducing the conservativeness of stability conditions, but also impose fewer restrictions on the functional construction. Leveraging these enhanced functionals along with certain inequalities, sufficient conditions are obtained to ensure the asymptotic stability of TSFSSI. Finally, the effectiveness of the proposed conditions is validated through two application examples.

关键词： new improved functional nonlinear systems stability state-dependent impulse T-S fuzzy model

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Brain Activation Differences of Six Basic Emotions Between 2D Screen and Virtual Reality Modalities

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 700-709页

作者： Xie, Jialan Lan, Ping Wang, Shiyuan Luo, Yutong Liu, Guangyuan Southwest Univ Inst Affect Comp & Informat Proc Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Southwest Univ Inst Affect Comp & Informat ProcMinist Educ Coll Elect & Informat EngnKey Lab Cognit & Person Fac PsycholChongqing Key Lab Nonlinear Circuits & Chongqing 400715 Peoples R China

To our knowledge, it has been widely studied in Screen-2D modality for the six basic emotions proposed by professor Paul Ekman, but there are only studies on their positive and negative valence in VR-3D modality. In this study, we will investigate whether the six basic emotions have stronger brain activation states in VR-3D modality than in Screen-2D modality. We designed an emotion-inducing experiment with six basic emotions (happiness, surprise, sadness, fear, anger, and disgust) to record the electroencephalogram (EEG) signals during watching VR-3D and Screen-2D videos. The power spectral density (PSD) was calculated to compare the brain activation differences between VR-3D and Screen-2D modalities during the induction of the six basic emotions. The results of statistical analysis of the relative power differences between VR-3D and Screen-2D modalities for each emotion revealed that both happiness and surprise presented greater differences in the $\alpha $ and $\gamma $ frequency bands, while sad, fear, disgust and anger all presented greater differences in the $\alpha $ and $\theta $ frequency bands, which are mainly observed in the frontal and occipital regions. On the other hand, the six emotions all yielded satisfactory classification accuracy (above 85%) by classification from a subset of power feature of the brain activation states in the same emotion between the two modalities. Overall, there are significant differences in the induction of same discrete emotions in VR-3D and Screen-2D modalities, with greater brain activation in VR-3D modalities. These findings provide a better understanding about the neural activity of discrete emotional tasks assessed in VR environments.

关键词： Videos Electroencephalography Visualization Three-dimensional displays Band-pass filters Particle measurements Atmospheric measurements Six basic emotions brain activation EEG virtual reality

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Distributed Computation for Sparse Recovery via Continuous-Time Neurodynamic Approach

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 3372-3383页

作者： Zhao, You Liao, Xiaofeng Zhou, Mingliang He, Xing Southwest Univ Sch Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

Sparsity has been extensively employed in multimedia sensing and computing in consumer electronics, signal and image processing, depth video codec, adaptive sparse-type equalizer, blind speech separation, and machine learning. Throughout this paper, we propose a novel distributed projection neurodynamic approach for solving the Basis Pursuit (BP) with flexible partition methods in a distributed manner. The proposed neurodynamic approach requires only that the network is undirected and connected, and no node can access the entire matrix simultaneously. First, we equivalently formulate the BP into a standard distributed optimization problem with a flexible partition-by-blocks method to obtain global information, and discuss the equivalence of their optimality conditions. Then, we propose a distributed continuous-time neurodynamic approach on the basis of primal-dual dynamical systems and projection operators, and also study its global convergence property. Finally, numerical experiments on sparse signals and image recovery further verify the effectiveness and superiority of our proposed neurodynamic approach.

关键词： Neurodynamics Optimization Convergence Sparse matrices Matching pursuit algorithms Standards Image reconstruction Distributed computation neurodynamic approach sparse recovery continuous-time global convergence

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