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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开通时间:..
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论文成果
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曹进德,李若霞,黄伟,魏运,郭建华,Traffic network equilibrium problems with demands uncertainty and capacity constraints of arcs by scalarization approaches:Science China Technological Sciences,2018.10.7,61(11):1642-1653
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李若霞,高兴宝,曹进德,Non-fragile state estimation for delayed fractional-order memristive neural networks:Applied Mathematics and Computation,2018.9.10,340(340):221-233
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李若霞,曹进德,Finite-time and fixed-time stabilization control of delayed memristive neural networks: Robust Analysis Technique:Neural Processing Letters,2018.6.1,47(3):1077-1096
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李若霞,曹进德,Finite-time stability analysis for Markovian jump memristive neural networks with partly unknown transition probabilities:IEEE Transactions on Neural Networks and Learning Systems,2017.12.1,28(12):2924-2935
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李若霞,曹进德,Alsaedi, Ahmad,Alsaadi, Fuad,Exponential and fixed-time synchronization of Cohen-Grossberg neural networks with time-varying delays and reaction-diffusion terms:Applied Mathematics and Computation,2017.11.15,313(0):37-51
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李若霞,曹进德,Alsaadi, Fuad,Alsaedi, Ahmad,Stability analysis of fractional-order delayed neural networks:Nonlinear Analysis: Modelling and Control,2017.10.7,22(4):505-520
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李若霞,曹进德,Alsaedi, Ahmed,Hayat, Tasawar,Non-fragile state observation for delayed memristive neural networks: continuous-time case and discrete-time case:Neurocomputing,2017.7.5,245(0):102-113
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曹进德,Fixed-time synchronization of delayed memristor-based recurrent neural networks:Science China-Information Sciences,2017.3.1,60(3):032201
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李若霞,曹进德,Alsaedi, Ahmed,Ahmad, Bashir,Passivity analysis of delayed reaction-diffusion Cohen-Grossberg neural networks via Hardy-Poincare inequality:Journal of the Franklin Institute-ENGINEERING AND APPLIED MATHEMATICS,2017.3.1,354(7):3021-3038
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李若霞,曹进德,TASAWAR HAYAT,Ahmad, Bashir,Alsaadi, Fuad E,Alsaedi, Ahmed,Nonlinear measure approach for the robust exponential stability analysis of interval inertial Cohen-Grossberg neural networks:COMPLEXITY,2016.10.7,21(S2):459-469
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