张芳海
个人信息
Personal information
副教授 硕士生导师
教师拼音名称：zhangfanghai
电子邮箱：
所在单位：电气与自动化工程学院
学历：研究生(博士)毕业
性别：男
联系方式：fhzhanghust@163.com
学位：博士学位
在职信息：在职
学科：控制理论与控制工程手机版二维码
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- [1] Fanghai Zhang，Zhigang Zeng，Multistability and stabilization of fractional-order competitive neural networks with unbounded time-varying delays:IEEE Transactions on Neural Networks and Learning Systems,2022,33(9):4515-4526.
- [2] Fanghai Zhang，Zhigang Zeng，Multiple Mittag-Leffler stability of delayed fractional-order Cohen-Grossberg neural networks via mixed monotone operator pair:IEEE Transactions on Cybernetics,2021,51(12):6333-6344.
- [3] Fanghai Zhang，Zhigang Zeng，Asymptotic stability and synchronization of fractional-order neural networks with unbounded time-varying delays:IEEE Transactions on Systems, Man, and Cybernetics: Systems,2021,51(9):5547-5556.
- [4] Fanghai Zhang，Tingwen Huang,Qiujie Wu,Zhigang Zeng，Multistability of delayed fractional-order competitive neural networks:Neural Networks,2021,140):325-335.
- [5] Fanghai Zhang，Zhigang Zeng,Dan Feng,Tingwen Huang，Multistability and robustness of complex-valued neural networks with delays and input perturbation:Neurocomputing,2021,447):319-328.
- [6] Fanghai Zhang，Zhigang Zeng，Robust stability of recurrent neural networks with time-varying delays and input perturbation:IEEE Transactions on Cybernetics,2021,51(6):3027-3038.
- [7] Fanghai Zhang，Zhigang Zeng，Multiple ψ-type stability of Cohen–Grossberg neural networks with unbounded time-varying delays:IEEE Transactions on Systems, Man, and Cybernetics: Systems,2021,51(1):521-531.
- [8] Fanghai Zhang，Zhigang Zeng，Multistability of fractional-order neural networks with unbounded time-varying delays:IEEE Transactions on Neural Networks and Learning Systems,2021,32(1):177-187.
- [9] Fanghai Zhang，Zhigang Zeng，Multiple Lagrange stability under perturbation for recurrent neural networks with time-varying delays:IEEE Transactions on Systems, Man, and Cybernetics: Systems,2020,50(6):2029-2041.
- [10] Fanghai Zhang，Zhigang Zeng，Multiple ψ-type stability and its robustness for recurrent neural networks with time-varying delays:IEEE Transactions on Cybernetics,2019,49(5):1803-1815.