郭津津

【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：10大信誉娱乐平台网站，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