Feng Li
Orcid: 0000-0002-1711-3891Affiliations:
- Anhui University of Technology, School of Electrical and Information Engineering, Ma'anshan, China
- Nanjing University of Science and Technology, School of Automation, China (PhD 2021)
- Anhui University of Technology, School of Electrical and Information Engineering, Ma'anshan, China (former)
According to our database1,
Feng Li
authored at least 39 papers
between 2015 and 2024.
Collaborative distances:
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Bibliography
2024
H∞ State Estimation for Two-Time-Scale Markov Jump Complex Networks Under Analog Fading Channels: A Hidden-Markov-Model-Based Method.
IEEE Trans. Circuits Syst. I Regul. Pap., August, 2024
Asynchronous Event-Triggered Passive Consensus of Semi-Markov Jump Multiagent Systems With Two-Time-Scale Feature Under DoS Attacks.
IEEE Syst. J., June, 2024
Fuzzy-model-based ℋ <sub>∞</sub> filtering for discrete-time singular Markov jump nonlinear systems against hybrid attacks.
J. Control. Decis., April, 2024
IEEE Trans. Circuits Syst. II Express Briefs, January, 2024
State estimation of singularly perturbed Semi-Markov jump coupled neural networks: A two-time-scale event-triggered approach.
Knowl. Based Syst., 2024
Hidden-Markov-model-based event-triggered output consensus for Markov jump multi-agent systems with general information.
J. Frankl. Inst., 2024
Multi-group consensus of multi-agent systems subject to semi-Markov jump topologies against hybrid cyber-attacks.
Inf. Sci., 2024
H∞ Filtering for Discrete-Time Singular Markov Jump Systems Under DoS Attacks and Its Application.
IEEE Access, 2024
522, Springer, ISBN: 978-981-97-0197-1, 2024
2023
Passivity-Based State Estimation of Markov Jump Singularly Perturbed Neural Networks Subject to Sensor Nonlinearity and Partially Known Transition Rates.
Neural Process. Lett., December, 2023
H<sub>∞ </sub> Secure Consensus of Two-Time-Scale Markov Jump Multi-agent Systems with Partially Unknown Transition Rates Against Hybrid Cyber-Attacks.
Neural Process. Lett., October, 2023
$\varvec{H}_{{\mathbf{\infty }}}$ Control for Interval Type-2 Fuzzy Singularly Perturbed Nonlinear Systems with Markov Jumping Parameters.
Int. J. Fuzzy Syst., October, 2023
Extended Dissipative Synchronization of Reaction-Diffusion Genetic Regulatory Networks Based on Sampled-data Control.
Neural Process. Lett., June, 2023
Mixed H∞ and passive consensus of Markov jump multi-agent systems under DoS attacks with general transition probabilities.
J. Frankl. Inst., 2023
Fuzzy multi-objective fault-tolerant control for nonlinear Markov jump singularly perturbed systems with persistent dwell-time switched transition probabilities.
Fuzzy Sets Syst., 2023
Mixed H∞/passive synchronization for persistent dwell-time switched neural networks via an activation function dividing method.
Appl. Math. Comput., 2023
2022
Finite-Time Fuzzy Control for Nonlinear Singularly Perturbed Systems With Input Constraints.
IEEE Trans. Fuzzy Syst., 2022
HMM-Based Fuzzy Control for Nonlinear Markov Jump Singularly Perturbed Systems With General Transition and Mode Detection Information.
IEEE Trans. Cybern., 2022
Stabilization of Discrete-Time Hidden Semi-Markov Jump Singularly Perturbed Systems With Partially Known Emission Probabilities.
IEEE Trans. Autom. Control., 2022
Stabilization of discrete-time semi-Markov jump singularly perturbed systems subject to actuator saturation and partially known semi-Markov kernel information.
J. Frankl. Inst., 2022
Reliable output feedback control for persistent dwell-time switched piecewise-affine systems against deception attacks.
Appl. Math. Comput., 2022
2021
Extended Dissipativity-Based Control for Hidden Markov Jump Singularly Perturbed Systems Subject to General Probabilities.
IEEE Trans. Syst. Man Cybern. Syst., 2021
Robust $\mathscr{H}_{\infty }$ Consensus for Markov Jump Multiagent Systems Under Mode-Dependent Observer and Quantizer.
IEEE Syst. J., 2021
A novel ε-dependent Lyapunov function and its application to singularly perturbed systems.
Autom., 2021
2020
Resilient Asynchronous $H_{\infty}$ Control for Discrete-Time Markov Jump Singularly Perturbed Systems Based on Hidden Markov Model.
IEEE Trans. Syst. Man Cybern. Syst., 2020
Fuzzy-Model-Based Output Feedback Reliable Control for Network-Based Semi-Markov Jump Nonlinear Systems Subject to Redundant Channels.
IEEE Trans. Cybern., 2020
Passivity-Based Control for Hidden Markov Jump Systems With Singular Perturbations and Partially Unknown Probabilities.
IEEE Trans. Autom. Control., 2020
H<sub>∞ </sub> Filtering for Markov Jump Neural Networks Subject to Hidden-Markov Mode Observation and Packet Dropouts via an Improved Activation Function Dividing Method.
Neural Process. Lett., 2020
Finite-Time Asynchronous H∞ Nonfragile Control for Markov Jump Systems with Time-Varying Mode Detection Probabilities.
Proceedings of the 59th IEEE Conference on Decision and Control, 2020
2019
Fuzzy-Model-Based H<sub>∞</sub> Control for Markov Jump Nonlinear Slow Sampling Singularly Perturbed Systems With Partial Information.
IEEE Trans. Fuzzy Syst., 2019
Synchronization control for Markov jump neural networks subject to HMM observation and partially known detection probabilities.
Appl. Math. Comput., 2019
2018
IEEE Trans. Fuzzy Syst., 2018
Fuzzy-Model-Based Nonfragile Control for Nonlinear Singularly Perturbed Systems With Semi-Markov Jump Parameters.
IEEE Trans. Fuzzy Syst., 2018
Slow State Variables Feedback Stabilization for Semi-Markov Jump Systems With Singular Perturbations.
IEEE Trans. Autom. Control., 2018
2017
A unified method to energy-to-peak filter design for networked Markov switched singular systems over a finite-time interval.
J. Frankl. Inst., 2017
2016
On dissipative filtering over unreliable communication links for stochastic jumping neural networks based on a unified design method.
J. Frankl. Inst., 2016
Finite-time l<sub>2</sub>-l<sub>∞</sub> tracking control for Markov jump repeated scalar nonlinear systems with partly usable model information.
Inf. Sci., 2016
2015
Finite-time <sub>H</sub><sub>∞</sub> synchronization control for semi-Markov jump delayed neural networks with randomly occurring uncertainties.
Neurocomputing, 2015
Non-fragile finite-time l<sub>2</sub>-l<sub>∞</sub> state estimation for discrete-time Markov jump neural networks with unreliable communication links.
Appl. Math. Comput., 2015