【个人简介】
郭津津,女,博士,校聘副教授。自2016年以来从事递归神经网络时变问题求解、数值计算与优化、机器人运动规划与控制等方面的相关研究,在IEEE Transactions on Systems, Man, and Cybernetics: Systems、IEEE Transactions on Industrial Informatics和IEEE Transactions on Industrial Electronics等国际期刊和会议上发表学术论文十余篇,并授权四项发明专利。
【教育背景】
2018.08-2022.06:中山大学,计算机科学与技术专业,工学博士学位。
2016.08-2018.06:中山大学,控制工程专业,工学硕士学位。
2012.09-2016.06:南昌大学,测控技术与仪器专业,工学学士学位。
【工作经历】
2022.08-至今:太阳成集团tyc33455cc,担任专职教师,从事教学科研工作。
【学科领域】
主要从事神经网络、神经动力学、数值计算、机器人方向研究。
【主讲课程】
《Python程序设计》、《自动控制原理》、《模拟电子技术》、《电路基础》等。
【项目课题】
1.国家自然科学基金面上项目,61976230,基于高精度时间离散公式与神经动力学的未来时变问题求解,2020-01至2023-12,60万元,在研,参与。
2.广东省自然科学基金面上项目,2019A1515012128,针对不同层未来时变问题的离散递归神经网络模型设计、实现与应用,2019-10至2022-09,10万元,在研,参与。
3.广州市重点领域研发计划项目,202007030004,基于类脑计算的视觉感知与控制关键技术研究及机器人集成,2020-04至2023-03,800万元,在研,参与。
【主要成果】
1. Guo J(郭津津), Zhang Y*. Stepsize interval confirmation of general four-step DTZN algorithm illustrated with future quadratic programming and tracking control of manipulators[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(3):1662-1670.
2. Guo J(郭津津), Qiu B, Chen J, Zhang Y*. Solving future different-layer nonlinear and linear equation system using new eight-node DZNN model[J]. IEEE Transactions on Industrial Informatics, 2020, 16(4):2280-2289.
3. Guo J(郭津津), Qiu B, Zhang Y*. Future different-layer linear equation and bounded inequality solved by combining Adams-Bashforth methods with CZNN model[J]. IEEE Transactions on Industrial Electronics, 2021, 68(2):1515-1524.
4. Guo J(郭津津), Qiu B, Hu C, Zhang Y*. Discrete-time nonlinear optimization via zeroing neural dynamics based on explicit linear multi-step methods for tracking control of robot manipulators[J]. Neurocomputing, 2020, 412:477-485.
5. Guo J(郭津津), Zhang Y, Qiu B*. Tracking control of ship course system using new six-step ZeaD (Zhang et al Discretization) formula with high precision[J]. Filomat, 2020, 34(15):5059-5071.
6. Zhang Y*, Guo J(郭津津), Qiu B, Li W. Zhang neural dynamics approximated by backward difference rules in form of time-delay differential equation[J]. Neural Processing Letters, 2019, 50(2): 1735-1753.
7. Qiu B, Guo J(郭津津), Yang S, Yu P, Tan N*. A novel discretized ZNN model for velocity-layer weighted multi-criteria optimization of robotic manipulators with multiple constraints[J]. IEEE Transactions on Industrial Informatics, doi: 10.1109/TII.2022.3197270.
8. Qiu B, Guo J(郭津津), Li X-D, Zhang Z, Zhang Y*. Discrete-time advanced zeroing neurodynamic algorithm applied to future equality-constrained nonlinear optimization with various noises[J]. IEEE Transactions on Cybernetics, 2022, 52(5):3539-3552.
9. Qiu B, Guo J(郭津津), Li X-D, Zhang Y*. New discretized zeroing neural network models for solving future system of bounded inequalities and nonlinear equations aided with general explicit linear four-step rule[J]. IEEE Transactions on Industrial Informatics, 2021, 17(8):5164-5174.
10. Chen J, Guo J(郭津津), Zhang Y*. General ten-instant DTDMSR model for dynamic matrix square root finding[J]. Cybernetics and Systems, 2021, 52(1):127-143.
11. Qiu B, Li X-D*, Guo J(郭津津), Tan N. New jerk-level configuration adjustment schemes applied to constrained redundant robots[J]. IEEE Transactions on Industrial Informatics, 2022, 18(4):2528-2538.
12. Guo J(郭津津), Qiu B, Ming L, Zhang Y*. Explicit linear dual-multistep methods applied to ZNN illustrated via discrete time-dependent linear and nonlinear inequalities system solving[C]. Proceedings of International Joint Conference on Neural Networks, pp. 9207394, Virtual, Glasow, Scotland, UK, July 19-24, 2020.
13. Guo J(郭津津), Qiu B, Yang M, Zhang Y*. Zhang neural network model for solving LQ decomposition problem of dynamic matrix with application to mobile object localization[C]. Proceedings of International Joint Conference on Neural Networks, pp. 9207394, Virtual, Shenzhen, China, July 18-22, 2021.
【联系方式】
邮箱:guojj2017@126.com