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A neural network based online learning and control approach for Markov jump systems

  • Published:2015-02 Issue: Volume:149 Page:116-123
  • ISSN:0925-2312
  • Container-title:Neurocomputing
  • language:en
  • Short-container-title:Neurocomputing

Author:

Zhong Xiangnan,He Haibo,Zhang Huaguang,Wang Zhanshan

Funder

National Science Foundation

Army Research Office

NSF-DFG Collaborative Research on “Autonomous Learning”

National Natural Science Foundation of China

IAPI Fundamental Research Funds

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Cited by 31 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Zero-sum game-based optimal control for discrete-time Markov jump systems: A parallel off-policy Q-learning method;Applied Mathematics and Computation;2024-04

2. Introduction;Robust Control for Discrete-Time Markovian Jump Systems in the Finite-Time Domain;2023

3. Neural-network-based model predictive control for consensus of nonlinear systems;Engineering Applications of Artificial Intelligence;2022-11

4. Zero‐sum game‐based security control of unknown nonlinear Markov jump systems under false data injection attacks;International Journal of Robust and Nonlinear Control;2022-10-17

5. Nonfragile Output Feedback Tracking Control for Markov Jump Fuzzy Systems Based on Integral Reinforcement Learning Scheme;IEEE Transactions on Cybernetics;2022
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