Fengzhen Tang

Shenyang Institute of Automation,
Chinese Academy of Sciences
Telephone: +86-24-23970211
Address: No.114 Nanta Street, Shenyang, Liaoning Province, China
Postcode:  110016

Research Areas

 Machine Learning,Artifical Intelligence, Intelligent Robotics, Computational Neuroscience

Recruiting research staff related to above research areas. Please send me your CV to my email box

You can also check the following links for detailed information: 

中国科学院沈阳自动化研究所2023-2024年招聘岗位需求(进行中)--中国科学院沈阳自动化研究所 (  (神经计算课题组)

中国科学院沈阳自动化研究所2023-2024年招聘简章--中国科学院沈阳自动化研究所 (


  • 2015 PhD in Computer Science,University of Birmingham, School of Computer Science, Birmingham,UK (Supervisor: Prof. Peter Tino; Second supervisor: Prof. Huanhuan Chen)

  • 2011 M.Sc in Computer Science and Technology,Northeastern University, Software College, Shenyang, China (Supervisor: Prof. Huiyan Jiang)

  • 2009 BEng in Software Engineering,Northeastern University , Software College, Shenyang, China


Work Experience

  • 2023 until present:Researcher in State Key Laboratory of Robotics, Shenyang Insitute of Automation Chinese Academy of Sciences, Shenyang, China

  • 2018 until 2022: Associate Researcher in State Key Laboratory of Robotics, Shenyang Insitute of Automation Chinese Academy of Sciences, Shenyang, China

  • 2015- 2017: Assistant Researcher in State Key Laboratory of Robotics, Shenyang Insitute of Automation Chinese Academy of Sciences, Shenyang, China

Teaching Experience

In Shenyang Institute of Automation, Chinse Academy of Science

  • Sep 2017 - Jan 2018. Lecturer. Course : Academic English for Postgraduates

In School of Computer Science, The University of Birmingham, UK.

  • Jan 2015 - May 2015. Teaching Associate. Course : Foundations of Computer Science and MSc/ICY Java Workshop.
  • Sep 2014 - Dec 2014. Demonstrator. Course : Mathematical Techniques for Computer Science and MSc/ICY Java Workshop.
  • Feb 2014 - May 2014. Demonstrator. Course : Introduction to Mathematics for Computer Science.
  • Sep 2013 - Dec 2013. Demonstrator. Course : Mathematical Techniques for Computer Science.
  • Jan 2012 - Mar 2013. Demonstrator. Course : Foundations of Computer Science
  • Sep 2012 - Dec 2012. Demonstrator. Course : Mathematical Techniques for Computer Science

In Software College, Northeastern University, Shenyang, China

  • 2009-2011 Teaching Assistant of Program Practice (IV)(Network Application Programming Based on TCP/IP)


Journal Papers

[25]  Zhihui Zhang, Fengzhen Tang*, Yiping Li, Xisheng Feng.A spatial transformation-based CAN model for information integration within grid cell modules.Cognitive Neurodynamics,, 2024.(SCI, 3区)

[24]Chenchen Wu, Ruming Zhang, Fengzhen Tang, Mengling Fan. Vibration optimization of cantilevered bistable composite shells based on machine learning.Engineering Applications of Artificial Intelligence,126:1-10,2023 (SCI, 2区Top)

[23] Zirui Zhang, Yinan Guo, Fengzhen Tang*.  Dimension Selection for EEG Classification in the SPD Riemannian Space Based on PSO. Knowledge-Based Systems, 279:110933,2023. (SCI, 1区Top)

[22] D Xu, F Tang, Y Li, Q Zhang, X Feng. FB-CCNN: A Filter Bank Complex Spectrum Convolutional Neural Network with Artificial Gradient Descent Optimization,Brain Sciences 13 (5), 780 (SCI, 3区)

[21]   Zhihui Zhang, Fengzhen Tang*, Yiping Li, Xisheng Feng. Modeling the grid cell activity based on cognitive space transformation. Cognitive Neurodynamics., 2023 (SCI, 3区)

[20] Dongcen Xu, Fengzhen Tang, Yiping Li, Qifeng Zhang and Xisheng Feng. An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey. Brain Sciences. 13(3):1-23, 2023 (SCI, 3区)

[19] Kai He, Wenxue Wang, Gang Li, Peng Yu, Fengzhen Tang, LianqingLiu. Knowledge-based hybrid connectionist models for morphologic reasoning. Machine Vision and Applications, 34(2):1-13,2023 (EI,SCI, 4区)

[18] Changbo Zhu, Ke Zhou, Fengzhen Tang, Yandong Tang, Xiaoli Li, Bailu Si. A Hierarchical Bayesian Model for Inferring and Decision Making in Multi-Dimensional Volatile Binary Environments. Mathematics. 10(24):1-35,2022 (SCI, 4区)

[17] Bingjie Zhang, Xiaoling Gong, Jian Wang∗, Fengzhen Tang*, Kai Zhang, Wei Wu. Nonstationary Fuzzy Neural Network Based on FCMnet Clustering and a Modified CG Method with Armijo-type Rule, Information Sciences, 608:313-338,2022, (SCI, 1区Top)

[16] F. Tang, P. Tiňo and H. Yu, "Generalized Learning Vector Quantization With Log-Euclidean Metric Learning on Symmetric Positive-Definite Manifold," in IEEE Transactions on Cybernetics,53(8):5178-5190,2023. (SCI, 1区Top)

[15] M. Fan, F.Tang*, et al. Riemannian Dynamic Generalized Space Quantization Learning. Pattern Recognition, 132:108932,2022. (SCI, 1区Top)

[14] Yinan Guo,Botao Jiao, Ying Tan,Pei Zhang,Fengzhen Tang*, “A Transfer Weighted Extreme Learning Machine for Imbalance Classification[J]”. International Journal of Intelligent System 1-22,2022 (SCI)

[13] Yazhou Hu, Fengzhen Tang, Jun Chen,Wenxue Wang. Quantum‑enhanced reinforcement learning for control: a preliminary study. Control Theory and Technology,19:455-464,2021 (SCI)

[12] F. Tang, H. Feng, P. Tino, B. Si, D. Ji: Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices.Neural Networks, 142105-118, 2021. (c) Elsevier [arXiv]code  (SCI, 1区Top)

[11] Y. Guo, Z. Zhang, F. Tang*: Kernelized Multiclass Support Vector MachinePattern Recognition, 117107988, 2021.  (SCI, 1区Top)

[10] Y. Guo, B. Jiao, J. Cheng, L. Yang, S. Yang and F. Tang: A Novel Oversampling Technique Based on the Manifold Distance for Class Imbalance Learning, International Journal of Bio-Inspired Computation, 18(3): 131-142, 2021. (SCI)

[9]    F. Tang, M. Fan, P. Tino: Generalized Learning Riemannian Space Quantization: A Case Study on Riemannian Manifold of SPD MatricesIEEE Transactions on Neural Networks and Learning Systems, 1 (32), pp. 281-292, 2021.  code (SCI, 1区Top)

[8]    T. Zeng, F. Tang, D. Ji, B. Si : NeuroBayesSLAM: Neurobiologically inspired Bayesian integration of multisensory information for robot navigation, Neural Networks, 126: 21–35, 2020 (SCI, 1区Top)

[7]    Dongye Zhao, F. Tang* , Bailu Si, Xisheng Feng. Learning joint space-time-frequency features for EEG decoding on small labeled data. Neural Networks,114: 67-77, 2019 (IF: 7.197, SCI 一区Top )

[6]     M. Jiang, S. Song, F. Tang, Y. Li, J. Liu, X. Feng: Scan registration for underwater mechanical scanning imaging sonar using symmetrical Kullback-Leibler divergence, Journal of Electronic Imaging, 28(1):013026-1-11, 2019 (SCI)

[5]     F. TangL. Adam, B. Si: Group Feature Selection with Multiclass Support Vector Machine. Neurocomputing, 317:42–49, 2018. (SCI, 2区Top)

[4]    F. Tang, P. Tino: Ordinal Regression based on Learning Vector Quantization, Neural Networks  93 : 76-88, 2017.(SCI,1区Top) 

[3]     F. Tang, P. Tino, P. A. Gutierrez, H. Chen: The Benefits of Modeling Slack Variables in SVMs. Neural Computation, 27: 954–981, 2015 (SCI)

[2]    H. Jiang ,F. Tang, L. Zou, Y.-W. Chen: Data De-noising Based on PCA-KNN Algorithm in Billet Surface Temperature Measurement. Applied Mathematics & Information Sciences, 7(2L): 455-458, 2013. 

[1]    X. Liu, H. Jiang, F. Tang: Parameters Optimization in SVM Based-on Ant Colony Optimization algorithm, International Conference on Advances in Computer Science and Engineering, 2010. Advanced Materials Research Vols. 121-122 pp.470-475, 2010

Conference Papers


[13] Mengling Fan, Fengzhen Tang, Xigang Zhao. Shrinkage Estimator based Riemannian dynamic generalized space quantization learning for Multi-Class Motor Imagery Classification. YAC 2023 (EI), accepted

[12] Zhihui Zhang, Fengzhen Tang, Yiping Li, Xisheng Feng. MVSOP: A new framework for integrating MVS into SLAM based on ORB and PF. YAC 2023 (EI), accepted

[11]Mengling Fan, Fengzhen Tang, Xigang Zhao. Prototype Based Linear Sub-Manifold Learning. IJCNN 2023. (EI, CCF 推荐C类)

[10] Fei Song, Jinyu Li, Fengzhen Tang, Yandong Tang, Bailu Si*,  Reexaming Indoor Mobile Robots from a Cognitive Perspective, ICRCV, 2023

[9] Jianjun Xu, Nanya Yan, Fengzhen Tang. An Improvement of Loop Closure Detection Based on BoW for RatSLAM, YAC2022, (EI)

[8]     D. Zhao, B. Si, F. Tang: Unsupervised Feature Learning for Visual Place Recognition in Changing Environments, IJCNN, 2019

[7]      F. Tang, B. Si, D, Ji: A Prey-Predator Model for Efficient Robot Tracking, 2017 IEEE International Conference on Robotics and Automation (ICRA 2017), 2017

[6]     G. Huang, B. Si, F. Tang: Model Learning based on Grid Cell Representations, ROBIO, 2017 

[5]     H. Chen, F. Tang, P. Tino, A. G. Cohn and X. Yao. Model metric co- learning for time series classification. IJCAI, 2015.(录用率28.8%) 

[4]     F. Tang, P. Tino and H. Chen: Learning Deterministically Constructed Echo State Networks . International Joint Conference on Neural Networks (IJCNN), 2014, pp. 77 - 83

[3]    F. Tang, P. Tino, P. A. Gutierrez and H. Chen: Support Vector Ordinal Regression using Privileged Information. In Proceedings of the 2014 European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pp. 253-258 

[2]     H. Chen, F. Tang, P. Tino, X. Yao: Model-based Kernel for Efficient Time Series Analysis. 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Accepted for oral presentation, 2013.(录用率14.6%) 

[1]     H. Jang, F. Tang, X. Zhang: Liver Cancer Identification Based on PSO-SVM Model. ICARCV 2010

Paper In Chinese

[6] 赵杭飘,徐剑君,李涛,唐凤珍*:多机协同的类脑定位建图方法,机器人,接收. 一级学报

[5] 王运梦,李涛, 徐剑君, 唐凤珍, 崔龙, 刘钊铭, 刘连庆, 微型仿生爬虫机器人类脑环境感知方法,科学通报, 68:3095-3106, 2023.一级学报

[4] 徐剑君,商亮,唐凤珍*:基于快速增量式视觉感知的类脑 SLAM ,信息与控制, 51(1):542-553. 2022. 一级学报

Xu Jianjun, SHANG Liang, TANG Fengzhen, Brain-inspired SLAM Based on Fast Incremental Visual Perception. Information and Control, 51(1):542-553. 2022 (Chinese)

[3]张驰,唐凤珍*:基于自适应编码的脉冲神经网络,计算机应用研究,39(2):593-597. 2022. 中文核心

 Chi Zhang,Fengzhen Tang* : Self-adaptive coding for spiking neural network, Application Research of Computers 39(2): 593-597, 2022 (Chinese)


Xiaochen Zhang, Fengzhen Tang*:Probabilistic Riemannian quantization method on manifold with log-Euclidean metric learning, Application Research of Computers 39(3):661-680,2022 (Chinese)

[1] 冯海峰,唐凤珍*:基于超参数学习的概率黎曼空间量化算法,计算机应用研究,38:45-48,2021. 中文核心

Research Interests

Statistical machine learning, deep learning, metric learning, feature selection

Time series analysis, EEG signal decoding, Brain-computer interface, Autonomous robots