李若霞
硕士生导师
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基本信息
教师英文名称:Ruoxia Li
所在单位:数学与统计学院
学历:博士研究生
办公地点:文津楼三段3201
性别:女
联系方式:ruoxiali1227@163.com
学位:理学博士学位
职称:助理研究员(自然科学)
在职信息:在职
毕业院校:东南大学
学科:运筹学与控制论
应用数学
应用数学
联系信息
通讯/办公地址:
邮箱:
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论文成果
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李若霞,薛长峰,R. Manivannan,曹进德,Quasi-stability and quasi-synchronization control of quaternion-valued fractional-order discrete-time memristive neural networks:Applied Mathematics and Computation,2020.12.13,395:DOI: 10.1016/j.amc.2020.125851
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李若霞,曹进德,Exponential H∞ State Estimation for Memristive Neural Networks: Vector Optimization Approach:IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2020.10.22,0:DOI: 10.1109/TNNLS.2020.3026707
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李若霞,曹进德,Passivity and Dissipativity of Fractional-Order Quaternion-Valued Fuzzy Memristive Neural Networks: Nonlinear Scalarization Approach:IEEE TRANSACTIONS ON CYBERNETICS,2020.9.17,0:DOI: 10.1109/TCYB.2020.3025439
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李若霞,曹进德,Exponential stabilization of inertial quaternion-valued Cohen-Grossberg neural networks: Lexicographical order method:International Journal of Robust and Nonlinear Control,2020.6.17,0:DOI: 10.1002/rnc.5049
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李若霞,高兴宝,曹进德,张凯,Exponential Stabilization Control of Delayed Quaternion-Valued Memristive Neural Networks: Vector Ordering Approach:Circuits, Systems, and Signal Processing,2020.3.1,39(3):1353-1371
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李若霞,高兴宝,曹进德,Quasi-state estimation and quasi-synchronization control of quaternion-valued fractional-order fuzzy memristive neural networks: Vector ordering approach:APPLIED MATHEMATICS AND COMPUTATION,2019.12.1,362(0):124572
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李若霞,高兴宝,曹进德,张凯,Dissipativity and exponential state estimation for quaternion-valued memristive neural networks:NEUROCOMPUTING,2019.9.21,363(21):236-245
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李若霞,高兴宝,曹进德,Exponential State Estimation for Stochastically Disturbed Discrete-Time Memristive Neural Networks: Multiobjective Approach:IEEE Transactions on Neural Networks and Learning Systems,2019.8.28,0:DOI:10.1109/TNNLS.2019.2938774
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李若霞,高兴宝,曹进德,Exponential Synchronization of Stochastic Memristive Neural Networks with Time-Varying Delays:NEURAL PROCESSING LETTERS,2019.8.1,50(1):459-475
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李若霞,高兴宝,曹进德,张凯,Stability analysis of quaternion-valued Cohen-Grossberg neural networks:Mathematical Methods in the Applied Sciences,2019.7.15,42(10):3721-3738
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