导航

  • 个人资料
  • 个人概况
  • 研究领域
  • 开授课程
  • 科研项目
  • 论文
  • 科技成果
  • 荣誉及奖励
  • 招生信息
头像

姓名：黄鹤

学位：博士

毕业院校：香港城市大学

电子邮箱：hhuang@suda.edu.cn

办公地址：电子楼315

联系电话：

1515 访问

个人资料

  • 院部/部门：电子信息学院
  • 联系电话：
  • 性别：男
  • 电子邮箱：hhuang@suda.edu.cn
  • 专业技术职务：
  • 办公地址：电子楼315
  • 毕业院校：香港城市大学
  • 通讯地址：江苏省苏州市十梓街1号
  • 学位：博士
  • 邮编：215006
  • 学历：研究生
  • 传真：

工作经历

工作经历：
  • 2009.9-至今,苏州大学，电子信息学院
  • 2003.4-2006.10,东南大学，计算机科学与工程学院

社会职务

社会职务：社会职务：

1. Editorial member: Neurocomputing

2. Associate editor: Circuits, Systems & Signal Processing

3. Associate editor: Neural Processing Letters

4. 编委：新一代信息技术

教育经历

教育经历：
  • 2006-2009,香港城市大学,博士

个人简介

个人简介：

黄鹤，毕业于香港城市大学，获哲学博士学位。现为苏州大学电子信息学院教授，多次受邀到香港城市大学和德州农机大学卡塔尔分校进行合作研究。

主持完成国家自然科学基金2项，江苏省自然科学基金面上项目2项，入选2016年度江苏高校“青蓝工程”优秀青年骨干教师培养对象。

曾获教育部自然科学奖二等奖1项（排名第4），在科学出版社出版专著1部，在国际期刊如IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Circuits and Systems: Part I, IEEE Transactions on Circuits and Systems: Part II, Neural Networks及国内外学术会议上发表学术论文90余篇，授权发明专利10余项。

受邀担任《新一代信息技术》、Neurocomputing、Circuits, Systems & Signal Processing以及Neural Processing Letters的编委或副主编。

指导的研究生获2017年中国电子学会优秀硕士论文奖和2018年江苏省优秀学术学位硕士学位论文奖。

社会职务

社会职务：社会职务：

1. Editorial member: Neurocomputing

2. Associate editor: Circuits, Systems & Signal Processing

3. Associate editor: Neural Processing Letters

4. 编委：新一代信息技术

研究领域

研究方向：

1. 神经网络

2. 深度学习

3. 优化算法

4. 模式识别

5. 图像处理

开授课程

开授课程：
  • 1、数值最优化理论,研究生
  • 2、信号与系统,本科生
  • 3、工程数学（复变）,本科生
课程教学（旧版）：

科研项目

科研项目：

论文

论文：
  • 1、Wenbo Yu, He Huang and Gangxiang Shen, “Deep spectral-spatial feature fusion based multi-scale adaptable attention network for hyperspectral feature extraction,” IEEE Transactions on Instrumentation and Measurement, in press, 2022.
  • 2、Ying Huang and He Huang,“Stacked attention hourglass network based robust facial landmark detection,” Neural Networks, in press, 2022
  • 3、Yongliang Zhang, He Huang and Gangxiang Shen, “Adaptive CL-BFGS algorithms for complex-valued neural networks,” IEEE Transactions on Neural Networks and Learning Systems, in press, 2021. DOI: 10.1109/TNNLS.2021.3135553
  • 4、Wenbo Yu, He Huang and Gangxiang Shen, “Multi-level dual-direction modifying variational autoencoders for hyperspectral feature extraction,” IEEE Geoscience and Remote Sensing Letters, vol. 19, no. 6010805, 2022.
  • 5、Wenbo Yu, Miao Zhang, and He Huang, “Accelerated adaptive feature balance technique based on TEMD for hyperspectral classification,” IEEE Geoscience and Remote Sensing Letters, vol. 19, no. 6012105, 2022.
  • 6、Zhongying Dong and He Huang, “A training algorithm with selectable search direction for complex-valued feedforward neural networks,” Neural Networks, vol. 137, pp. 75-84, 2021.
  • 7、Yongliang Zhang and He Huang, “Adaptive complex-valued stepsize based fast learning of complex-valued neural networks,” Neural Networks, vol. 124, pp. 233-242, 2020.
  • 8、He Huang, Tingwen Huang and Yang Cao, “Reduced-order filtering of delayed static neural networks with Markovian jumping parameters,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no.11, pp. 5606-5618, 2018.
  • 9、He Huang, Tingwen Huang and Xiaoping Chen, “Reduced-order state estimation of delayed recurrent neural networks,” Neural Networks, vol. 98, pp. 59-64, 2018.
  • 10、Xusheng Qian, He Huang, Xiaoping Chen and Tingwen Huang, “Efficient construction of sparse radial basis function neural networks using L1-regularization,” Neural Networks, vol. 94, pp. 239-254, 2017.
  • 11、Xusheng Qian, He Huang, Xiaoping Chen and Tingwen Huang, “Generalized hybrid constructive learning algorithm for multioutput RBF networks,” IEEE Transactions on Cybernetics, vol. 47, no. 11, pp. 3634-3648, 2017.
  • 12、Jinxiang Zha, He Huang and Yujie Liu, “A novel window function for memristor model with application in programming analog circuits,” IEEE Transactions on Circuits and Systems Part II, vol. 63, no. 5, pp. 423-427, 2016.
  • 13、He Huang, Tingwen Huang and Xiaoping Chen, “Further result on guaranteed H∞ performance state estimation of delayed static neural networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 6, pp. 1335-1341, 2015.
  • 14、He Huang, Tingwen Huang, Xiaoping Chen and Chunjiang Qian, “Exponential stabilization of delayed recurrent neural networks: A state estimation based approach,” Neural Networks, vol. 48, pp. 153-157, 2013.
  • 15、He Huang, Tingwen Huang and Xiaoping Chen, “A mode-dependent approach to state estimation of recurrent neural networks with Markovian jumping parameters and mixed delays,” Neural Networks, vol. 46, pp. 50-61, 2013.
  • 16、He Huang, Tingwen Huang and Xiaoping Chen, “Guaranteed H∞ performance state estimation of delayed static neural networks,” IEEE Transactions on Circuits and Systems Part II, vol. 60, no. 6, pp. 371-375, 2013.
  • 17、He Huang, Tingwen Huang and Xiaoping Chen, “Global exponential estimates of delayed stochastic neural networks with Markovian switching,” Neural Networks, vol. 36, no. 1, pp. 136-145, 2012.
  • 18、He Huang, Gang Feng and Xiaoping Chen, “Stability and stabilization of Markovian jump systems with time delay via new Lyapunov functionals,” IEEE Transactions on Circuits and Systems Part I, vol. 59, no. 10, pp. 2413-2421, 2012.
  • 19、He Huang, Gang Feng and Jinde Cao, “State estimation for static neural networks with time-varying delay,” Neural Networks, vol. 23, no. 10, pp. 1202-1207, 2010.
  • 20、He Huang and Gang Feng, “A scaling parameter approach to delay-dependent state estimation of delayed neural networks,” IEEE Transactions on Circuits and Systems Part II, vol. 57, no. 1, pp. 36-40, 2010.
  • 21、He Huang and Gang Feng, “Delay-dependent H∞ and generalized H2 filtering for delayed neural networks”, IEEE Transactions on Circuits and Systems Part I, vol. 56, no. 4, pp. 846-857, 2009.
  • 22、He Huang and Gang Feng, “Synchronization of nonidentical chaotic neural networks with time delays,” Neural Networks, vol. 22, pp. 869-874, 2009.
  • 23、He Huang and Gang Feng, “Delay-dependent H∞ and generalized H2 filtering for delayed neural networks”, IEEE Transactions on Circuits and Systems Part I, vol. 56, no. 4, pp. 846-857, 2009.
  • 24、He Huang, Gang Feng and Jinde Cao, “Robust state estimation for uncertain neural networks with time-varying delay”, IEEE Transactions on Neural Networks, vol. 19, no. 8, pp. 1329-1339, 2008.
  • 25、He Huang, Daniel W. C. Ho and Yuzhong Qu, “Robust stability of stochastic delayed additive neural networks with Markovian switching”, Neural Networks, vol. 20, no. 7, pp. 799-809,2007
  • 26、He Huang, Daniel W. C. Ho and James Lam, “Stochastic stability analysis of fuzzy Hopfield neural networks with time-varying delay”, IEEE Transactions on Circuits and Systems Part II, vol. 52, no. 5, pp. 251-255, 2005.
  • 27、He Huang, Daniel W. C. Ho and Jinde Cao, “Analysis of global exponential stability and periodic solutions of neural networks with time-varying delays”, Neural Networks, vol. 18, no. 2, pp. 161-170, 2005.

科技成果

软件著作 著作：
  • 1、时滞递归神经网络的状态估计理论与应用,2014,科学出版社
 专利 专利：
  • 1、基于AP-NAG算法的复值神经网络信道均衡器设计方法，2022
  • 2、光纤通信系统的复信道均衡器设计方法，2022
  • 3、基于时空增强网络的视频动作识别方法，2022
  • 4、基于自适应L-BFGS 算法的深度神经网络的批量学习方法，2022
  • 5、复值信道均衡器的设计方法，2021
  • 6、基于深度学习的人脸特征点检测方法，2021
  • 7、基于生成对抗网络的多姿态面部表情识别方法，2020
  • 8、基于深度学习的一阶段车牌检测识别方法，2020
  • 9、一种基于简化卷积神经网络的车牌自动识别系统，2018

荣誉及奖励

荣誉及奖励：
  • 1、2017年度中国电子学会优秀硕士学位论文指导老师
  • 2、2018年度江苏省优秀学术学位硕士学位论文指导老师
  • 3、神经网络模型的动态特征及优化计算理论,2008,教育部自然科学奖二等奖

招生信息

招生信息：

欢迎电子信息、计算机、自动化和应用数学等专业的同学报考研究生，有兴趣的同学请通过电子邮件联系。

 招生信息（旧版）：