个人简介
 朱修彬，工学博士，西安电子科技大学机电工程学院副教授，硕士生导师，系统控制与自动化研究团队（SCAG）核心成员。拥有独特的“产业+学术”复合背景：硕士毕业后，曾在IT领域长期从事数据库开发与研究工作，积累了深厚的工程实践经验；2018年于西安电子科技大学获得工学博士学位后留校任教，致力于将前沿人工智能技术与复杂系统控制理论相融合。

研究方向：智能控制与人工智能交叉
 我们正处在一个“万物智联”的时代，传统的控制理论正在向数据驱动、自主学习、智能决策的方向演进。课题组围绕这一趋势，聚焦于智能控制与人工智能的交叉领域，旨在利用先进的智能算法解决复杂系统中的感知、决策与控制难题。核心研究方向包括：

• 智能感知与理解：聚焦目标检测、目标重识别（ReID）等核心技术，结合深度学习与视觉语言模型，实现对复杂场景（如无人系统、智能制造）中目标的精准感知与持续跟踪；

• 数据驱动的控制与决策：基于时序数据分析、联邦学习、异常值检测，在保障数据隐私的同时，实现对系统异常状态的精准识别与分布式协同控制；

• 可解释人工智能（XAI）：针对控制领域对安全性和可靠性的高要求，探索模型可解释性与融合知识的机器学习，让“黑盒”模型变得透明可信，服务于高可靠性的工业控制场景；

• 医工交叉：利用深度学习技术、大语言模型、视觉语言模型处理医学信号与医学影像，探索智能控制技术在精准医疗领域的创新应用。

学术资源与平台
 依托西安电子科技大学机电工程学院及系统控制与自动化研究团队（SCAG），课题组与国内外知名高校及科研院所保持紧密合作，能为学生提供前沿的学术视野和优质的科研条件。近年来在IEEE汇刊等高水平期刊发表系列论文，并承担国家自然科学基金、173计划技术领域基金等多项国家级、省部级课题，以及企业横向课题，确保理论研究与工程应用的紧密结合。

学术服务
 现任国际权威期刊 《Knowledge-Based Systems》副主编；担任IEEE TFS, IEEE TCYB, IEEE TNNLS等多个顶级期刊及IEEE CDC等国际会议审稿人，可为学生提供良好的学术交流平台。

招生信息

我们寻找对“智能控制”充满热情的学生，如果你对控制理论或人工智能技术充满兴趣，欢迎加入研究团队。

希望你具备：

1. 坚实的数理基础：熟悉高等数学、线性代数、概率与统计；

2. 良好的英语能力：具备阅读、写作与交流能力；

3. 优秀的编程能力：熟练掌握C、Matlab、Python等至少一门语言；

4. 强烈的求知欲：对控制理论或人工智能交叉的前沿探索有浓厚兴趣，乐于钻研。

招生名额：每年招收硕士研究生3~4名。

期刊论文：

[1] X. B. Zhu, W. Pedrycz, and Z. W. Li, “A design of granular Takagi-Sugeno fuzzy model through the synergy of fuzzy subspace clustering and optimal allocation of information granularity,” IEEE Transactions on Fuzzy Systems, vol. 26, no. 5, pp. 2499-2509, 2018.

[2] X. B. Zhu, W. Pedrycz, and Z. W. Li, “Granular encoders and decoders: A study in processing information granules,” IEEE Transactions on Fuzzy Systems, vol.25, no. 5, pp. 1115-1126, 2017.

[3] X. B. Zhu, W. Pedrycz, and Z. W. Li, “Granular models and granular outliers”, IEEE Transactions on Fuzzy Systems, vol. 26, no. 6, pp. 3835-3846, 2018.

[4] X. B. Zhu, W. Pedrycz, and Z. W. Li, “Granular representation of data: A design of families of epsilon-information granules,” IEEE Transactions on Fuzzy Systems, vol. 26, no. 4, pp. 2107-2119, 2018.

[5] X. B. Zhu, W. Pedrycz, and Z. W. Li, “A Development of Hierarchically Structured Granular Models Realized through Allocation of Information Granularity,” IEEE Transactions on Fuzzy Systems, vol. 29, no. 12, pp. 3845-3858, 2020.

[6] X. B. Zhu, D. Wang, W. Pedrycz, and Z. W. Li, “Horizontal Federated Learning of Takagi-Sugeno Fuzzy Rule-based Models,” IEEE Transactions on Fuzzy Systems, vol. 30, no. 9, pp. 3537-3547, 2022.

[7] X. B. Zhu, D. Wang, W. Pedrycz, and Z. W. Li, “Fuzzy rule-based local surrogate models for black-box model explanation,” IEEE Transactions on Fuzzy Systems, vol. 31, no. 6, pp. 2056-2064, 2023.

[8] X. B. Zhu, X. C. Hu, L. Yang, W. Pedrycz, and Z. W. Li, “A development of fuzzy rule-based regression models through using decision trees,” IEEE Transactions on Fuzzy Systems

[9] G. Zhang, X. B. Zhu, L. Yin, W. Pedrycz, and Z. W. Li, “Fuzzy Prediction Model in Privacy Protection: Takagi-Sugeno Rules Model via Differential Privacy,” IEEE Transactions on Fuzzy Systems

[10] X. C. Hu, X. B. Zhu, Lan Yang, W. Pedrycz, and Z. W. Li, “A Design of Fuzzy Rule-based Classifier for Multi-class Classification and its Realization in Horizontal Federated Learning,” IEEE Transactions on Fuzzy Systems, 2024.

[11] D. Wang, Monika Richter, X. B. Zhu, W. Pedrycz, Adam Gacek, Aleksander Sobotnicki, and Z. W. Li, “Detecting Characteristic Points for the Analysis of Bioimpedance Signal Through a Synergy of Fuzzy Rule-based models and Granular Neural Networks,” IEEE Transactions on Fuzzy Systems, 2024.

[12] Lan Yang, X. B. Zhu, W. Pedrycz, and Z. W. Li, and X. C. Hu, “A Granular Aggregation of Multifaceted Gaussian Process Models,” IEEE Transactions on Fuzzy Systems, 2024.

[13] Huimin Zhang, X. C. Hu, X. B. Zhu, Xinwang Liu, and W. Pedrycz, “Application of Gradient Boosting in the Design of Fuzzy Rule-based Regression Models,” IEEE Transactions on Knowledge and Data Engineering, 2024.

[14] X. B. Zhu, W. Pedrycz, and Z. W. Li, “Granular data description: Designing ellipsoidal information granules,” IEEE Transactions on Cybernetics, vol. 47, no. 12, pp. 4475-4484, 2017. 

[15] X. B. Zhu, W. Pedrycz, and Z. W. Li, “A development of granular input space in system modeling,” IEEE transactions on Cybernetics, vol. 51, no. 3, pp. 1639-1650, 2021.

[16] X. B. Zhu, W. Pedrycz, and Z. W. Li, “A two-stage approach for constructing type-2 information granules,” IEEE transactions on Cybernetics, vol.52, no.4, pp. 2214-2224, 2022.

[17] X. B. Zhu, W. Pedrycz, and Z. W. Li, “A granular approach to interval output estimation for rule-based fuzzy models,” IEEE transactions on Cybernetics, vol. 52, no. 7, pp. 7029-7038, 2022.

[18] X. B. Zhu, D. Wang, W. Pedrycz, and Z. W. Li, “A design of granular classifier based on granular data descriptors,” IEEE Transactions on Cybernetics, vol. 53, no. 3, pp. 1790-1801, 2023.

[19] P. Nie, X. B. Zhu, W. Pedrycz, and Z. W. Li, “Optimization of granulation-degranulation mechanism through neurocomputing,” IEEE transactions on Cybernetics, vol. 52, no. 6, pp. 2168-2267, 2022.

[20] X. B. Zhu, W. Pedrycz, Ting Qu, and Z. W. Li, “From numeric to granular models: A Quest for error and performance analysis,” IEEE transactions on Cybernetics, vol. 54, no. 1, pp. 150-161, 2024.

[21] K. Y. Wu, X. C. Hu, L. Y. Liu, X. B. Zhu, J. C. Huang, and W. Pedrycz, “Local Surrogate Models with Residual Fuzzy Rules for Model Agnostic Explanations,” IEEE transactions on Cybernetics, 2025.

[22] X. B. Zhu, W. Pedrycz, and Z. W. Li, “Development and analysis of neural networks realized in the presence of granular data,” IEEE Transactions on Neural Networks and Learning Systems, vol.31 no.9, pp. 3606-3619, 2020.

[23] X. B. Zhu, W. Pedrycz, and Z. W. Li, “Construction and evaluation of information granules: From the perspective of clustering,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol.52, no.3, pp.2024-2037, 2022.

[24] X. B. Zhu, D. Wang, W. Pedrycz, and Z. W. Li, “Design and development of granular fuzzy rule-based models for knowledge transfer,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 2, pp. 704-715, 2023.

[25] X. B. Zhu, D. Wang, W. Pedrycz, and Z. W. Li, “Privacy-preserving realization of fuzzy clustering and fuzzy modeling through vertical federated learning,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, doi：10.1109/TSMC.2023.3320680, 2023.

[26] D. Wang, X. B. Zhu, W. Pedrycz, and Z. W. Li, “Transfer learning realized with nonlinearly transformed input space,” IEEE Transactions on Emerging Topics in Computing, vol. 11, no. 2, pp. 448-460, 2023.

[27] X. B. Zhu, W. Pedrycz, and Z. W. Li, “Fuzzy clustering with nonlinearly transformed data,” Applied Soft Computing, vol. 61, pp. 364-376, Dec 2017.

[28] D. Wang, P. Nie, X. B. Zhu, W. Pedrycz, and Z. W. Li, “Designing of higher order information granules through clustering heterogeneous granular data,” Applied Soft Computing, vol. 112, pp. 107820, 2021. 

[24] X. Y. Han, X. B. Zhu, W. Pedrycz, and Z. W. Li, “A three-way classification with fuzzy decision trees,” Applied Soft Computing, vol. 132, pp. 109788, 2022.

[19] G. Zhang, X. B. Zhu, L. Yin, W. Pedrycz, and Z. W. Li, “Granular data representation under privacy protection: Tradeoff between data utility and privacy via information granularity,” Applied Soft Computing, vol. 131, pp. 109808, 2022.

[30] D. Wang, X. B. Zhu, W. Pedrycz, Adam Gacek, Aleksander Sobotnicki and Z. W. Li, “Modeling and Analysis of Cardioimpedance Signals Using Polynomial Models and Fuzzy Rule-based Models,” Applied Soft Computing, vol. 144, no. C, pp. 110482, 2023. 

[31] X.Y. Han, X. B. Zhu, W. Pedrycz, and Z. W. Li, “A Design of Fuzzy Rule-based Classifier Optimized through Softmax Function and Information Entropy,” Applied Soft Computing, 2024.

[32] D. Wang, Kai Yu, X. B. Zhu, Zhenhua Yu, “Optimal solutions to granular fuzzy relation equations with fuzzy logic operations,” Applied Soft Computing, Volume 163, September 2024, 111861.

[33] D. Wang, X. B. Zhu, W. Pedrycz, and Z. W. Li, “A Randomization Mechanism for Realizing Granular Models in Distributed System Modeling,” Knowledge-based Systems, vol. 232, pp. 107376, 2021.

[34] T. Y. Liu, X. B. Zhu, W. Pedrycz, Z. W. Li, “A design of information granule-based under-sampling method in imbalanced data classification,” Soft Computing, vol.24, no. 22, pp. 17333-17347, 2020.

[35] Huimin Zhang, X. B. Zhu, “Development and evaluation of M + 1-way classification mechanism realized through identifying foreign patterns,” Soft computing, vol. 27, no. 4659–4668, 2023.

[36] D. Wang, X. B. Zhu, W. Pedrycz, Zhenhua Yu, Z. W. Li, “A development of coordinate-based fuzzy encoding algorithm in compression of grayscale images,” Soft Computing, doi: 10.1007/s00500-023-09106-8, 2023.

[37] D. Wang, X. B. Zhu, W. Pedrycz, Zhenhua Yu and Z. W. Li, “Development of granular fuzzy relation equations based on a subset of data,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 8, pp. 1416-1427, 2021.

[39] T. L. Jing, W. Pedrycz, X. B. Zhu, G. Succi, and Z. W. Li, “Linguistic models: optimization with the use of conditional Fuzzy C-Means,” IEEE Transactions on Emerging Topics in Computational Intelligence, DOI:10.1109/TETCI.2023.3265391, 2023.

 

会议论文

[1] Xiubin Zhu, Witold Pedrycz, and Zhiwu Li, Granular description of data: Building information granules with the aid of the principle of justifiable granularity [C]// IEEE International Conference on Fuzzy Systems. IEEE, 2016:969–976.  

[2] Runqi Nan, Xiubin Zhu, Dan Wang, and Ziyue Ma, “A realization of self-face diagnosis algorithm based on chinese medicine theory,” the 5th International Conference on Artificial Intelligence and Big Data (ICAIBD 2022), May 27-30, 2022, Chengdu, China

[3] Zhidong Zhang, Xiubin Zhu, Ding Liu, “Model of Gradient Boosting Random Forest Prediction,” 2022 IEEE International Conference on Networking, Sensing and Control (IEEE ICNSC 2022), Shanghai, China