VLDB 2026 Research / reviewers in the wild / expert
Lulu Li 0001
dblp:03/1339-1
· DBLP profile ↗
26ranked-venue papers
10as first author
13since 2021 · last 2026
0000-0001-8965-2766ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 19 · 6 first-author · 9 since 2021Databases, data management, data science and information retrieval · 3 · 2 first-author · 2 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author · 1 since 2021Computer networks · 1 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Quantized Security Consensus of Multiagent Systems With Proportional Delay Under DoS Attacks
Lulu Li 0001, Jianquan Lu, Tingwen Huang |
IEEE Internet Things J. | 2 |
| 2025 | Stability analysis of inertial delayed neural network with delayed impulses via dynamic event-triggered impulsive control
Mengyao Shi, Lulu Li 0001, Jinde Cao, Liang Hua, Mahmoud A. Abdel-Aty |
Neurocomputing | 2 |
| 2025 | Secure synchronization of stochastic neural networks under deception attacks: An event-based intermittent impulsive approach
Xiaotao Zhou, Jieqing Tan, Lulu Li 0001, Yangang Yao |
Neurocomputing | 3 |
| 2024 | State estimation of switched finite-field networks: A multi-valued particle filter approach
Lulu Li 0001, Jianquan Lu |
Inf. Sci. | 2 |
| 2024 | Exponential Synchronization of Coupled Inertial Neural Networks With Hybrid Delays and Stochastic ImpulsesabstractThe synchronization problem of the coupled delayed inertial neural networks (DINNs) with stochastic delayed impulses is studied. Based on the properties of stochastic impulses and the definition of average impulsive interval (AII), some synchronization criteria of the considered DINNs are obtained in this article. In addition, compared with previous related works, the requirement on the relationship among the impulsive time intervals, system delays, and impulsive delays is removed. Furthermore, the potential effect of impulsive delay is studied by rigorous mathematical proof. It is shown that within a certain range, the larger the impulsive delay, the faster the system converges. Numerical examples are provided to show the correctness of the theoretical results. Lulu Li 0001, Jinde Cao, Jianlong Qiu, Yifan Sun 0004 |
IEEE Trans. Neural Networks Learn. Syst. | 1 |
| 2023 | Stability of the Caputo fractional-order inertial neural network with delay-dependent impulses
Lingao Luo, Lulu Li 0001, Wei Huang 0020 |
Neurocomputing | 2 |
| 2023 | A Unified Synchronization Criterion for Reaction-Diffusion Neural Networks with Time-Varying Impulsive Delays and System Delay
Lulu Li 0001, Wei Huang 0020 |
Neural Process. Lett. | 2 |
| 2023 | Quantized Synchronization Control of Networked Nonlinear Systems: Dynamic Quantizer Design With Event-Triggered MechanismabstractThis article investigates the quantized control issue for synchronizing a networked nonlinear system. Due to limited energy and channel resources, the event-triggered control (ETC) method and input quantization are simultaneously taken into account in this article. First, a dynamic quantizer, which discretely adjusts its parameters online and possesses a finite quantization range, is introduced to achieve exact synchronization, rather than quasisynchronization. Next, a new distributed Zeno-free ETC strategy is proposed based on the dynamic quantizer. Then, two different situations, that is, the quantizer is designed with/without the network topology information, are, respectively, discussed. Synchronization criteria are, respectively, derived under such two circumstances by using the Lyapunov method. Finally, numerical examples are provided to show the effectiveness of the theoretical results. Yifan Sun 0004, Lulu Li 0001, Daniel W. C. Ho |
IEEE Trans. Cybern. | 2 |
| 2022 | Stability of inertial delayed neural networks with stochastic delayed impulses via matrix measure method
Lulu Li 0001, Jinde Cao |
Neurocomputing | 2 |
| 2022 | Robust set stability of probabilistic Boolean networks under general stochastic function perturbation
Lulu Li 0001, Anguo Zhang, Jianquan Lu |
Inf. Sci. | 1 |
| 2022 | Exponential synchronization of coupled neural networks under stochastic deception attacks
Lulu Li 0001 |
Neural Networks | 2 |
| 2022 | Dynamic Quantization Driven Synchronization of Networked Systems Under Event-Triggered MechanismabstractThis paper focuses on the synchronization control issue of networked systems. Due to the imperfect network environment, continuous or precise transmission is impractical, and intermittent communication scheme under state quantization needs to be considered. First, an easy-to-implement dynamic quantizer, which possesses finite quantization level, is designed to deal with the imprecise information sharing. Next, based on the designed quantizer, a dynamic estimator is introduced to estimate the real-time state information and generate control inputs. An event-based communication scheme is utilized to ensure that the estimate error does not exceed a certain threshold. Then, detailed design of the dynamically quantized controller is given to achieve exact synchronization, and corresponding distributed design is also provided to improve the feasibility. Moreover, some results for practical synchronization are derived. Finally, the validity of our theoretical results is illustrated by two numerical examples. Lulu Li 0001, Yifan Sun 0004, Jianquan Lu, Jinde Cao |
IEEE Trans. Circuits Syst. I Regul. Pap. | 1 |
| 2021 | Synchronization of Coupled Memristor Neural Networks with Time Delay: Positive Effects of Stochastic Delayed Impulses
Lulu Li 0001, Yifan Sun 0004, Wei Huang 0020 |
Neural Process. Lett. | 1 |
| 2020 | Event-based bipartite multi-agent consensus with partial information transmission and communication delays under antagonistic interactions
Lulu Li 0001, Xiaoyang Liu 0002, Wei Huang 0020 |
Sci. China Inf. Sci. | 1 |
| 2020 | Complete synchronization of coupled Boolean networks with arbitrary finite delaysabstractIn this study, the complete synchronization problem of coupled delayed Boolean networks (CDBNs) is investigated. The state delays and output delays may not be equal, and the state delay in each Boolean network may be different in the proposed CDBN model. Based on the semi-tensor product of matrices, a necessary and sufficient condition for the complete synchronization of CDBNs is obtained. Then, an efficient algorithm for solving the synchronization of CDBNs is provided. Finally, numerical examples are presented to demonstrate the effectiveness of our algorithm. Jie Liu 0077, Lulu Li 0001, Habib Fardoun |
Frontiers Inf. Technol. Electron. Eng. | 2 |
| 2020 | Quasi-bipartite synchronization of signed delayed neural networks under impulsive effects
Guohong Mu, Lulu Li 0001 |
Neural Networks | 2 |
| 2020 | Exponential synchronization of neural networks with time-varying delays and stochastic impulses
Yifan Sun 0004, Lulu Li 0001, Xiaoyang Liu 0002 |
Neural Networks | 2 |
| 2019 | Synchronization of impulsive coupled complex-valued neural networks with delay: The matrix measure method
Lulu Li 0001, Xiaohong Shi, Jinling Liang |
Neural Networks | 1 |
| 2019 | Synchronization of Coupled Complex-Valued Impulsive Neural Networks with Time Delays
Lulu Li 0001, Guohong Mu |
Neural Process. Lett. | 1 |
| 2017 | Event-Triggered Schemes on Leader-Following Consensus of General Linear Multiagent Systems Under Different TopologiesabstractThis paper investigates the leader-following consensus for multiagent systems with general linear dynamics by means of event-triggered scheme (ETS). We propose three types of schemes, namely, distributed ETS (distributed-ETS), centralized ETS (centralized-ETS), and clustered ETS (clustered-ETS) for different network topologies. All these schemes guarantee that all followers can track the leader eventually. It should be emphasized that all event-triggered protocols in this paper depend on local information and their executions are distributed. Moreover, it is shown that such event-triggered mechanism can significantly reduce the frequency of control's update. Further, positive inner-event time intervals are assured for those cases of distributed-ETS, centralized-ETS, and clustered-ETS. In addition, two methods are proposed to avoid continuous communication between agents for event detection. Finally, numerical examples are provided to illustrate the effectiveness of the ETSs. Wenying Xu, Daniel W. C. Ho, Lulu Li 0001, Jinde Cao |
IEEE Trans. Cybern. | 3 |
| 2016 | Finding graph minimum stable set and core via semi-tensor product approach
Jie Zhong 0005, Jianquan Lu, Chi Huang, Lulu Li 0001, Jinde Cao |
Neurocomputing | 4 |
| 2016 | A consensus recovery approach to nonlinear multi-agent system under node failure
Lulu Li 0001, Daniel W. C. Ho, Jianquan Lu |
Inf. Sci. | 1 |
| 2016 | Pinning cluster synchronization in an array of coupled neural networks under event-based mechanism
Lulu Li 0001, Daniel W. C. Ho, Jinde Cao, Jianquan Lu |
Neural Networks | 1 |
| 2015 | Fault-Tolerant Consensus of Multi-Agent System With Distributed Adaptive ProtocolabstractIn this paper, fault-tolerant consensus in multi-agent system using distributed adaptive protocol is investigated. Firstly, distributed adaptive online updating strategies for some parameters are proposed based on local information of the network structure. Then, under the online updating parameters, a distributed adaptive protocol is developed to compensate the fault effects and the uncertainty effects in the leaderless multi-agent system. Based on the local state information of neighboring agents, a distributed updating protocol gain is developed which leads to a fully distributed continuous adaptive fault-tolerant consensus protocol design for the leaderless multi-agent system. Furthermore, a distributed fault-tolerant leader-follower consensus protocol for multi-agent system is constructed by the proposed adaptive method. Finally, a simulation example is given to illustrate the effectiveness of the theoretical analysis. Daniel W. C. Ho, Lulu Li 0001, Ming Liu 0014 |
IEEE Trans. Cybern. | 3 |
| 2011 | Cluster synchronization in an array of coupled stochastic delayed neural networks via pinning control
Lulu Li 0001, Jinde Cao |
Neurocomputing | 1 |
| 2009 | Cluster synchronization in an array of hybrid coupled neural networks with delay
Jinde Cao, Lulu Li 0001 |
Neural Networks | 2 |