工业系统智能控制

[发表论文]

2021

[1] 唐伟,王立忠,庄健,王庭凯. 无刷直流电机的模糊自整定MRPID转速控制方法研究[J],中国机械工程,202,32(15):1786-1792,1800.(DOI: 10.3969/j.issn.1004-132.2021.15.003)

[1].Yu J, Zhuang J, Yu DH. State feedback integral control for a rotary direct drive servo valve using a Lyapunov function approach[J]. ISA Transactions, 2015,54(1):207-217. 

[2].Yu J, Zhuang J, Yu DH. Modeling and analysis of a rotary direct drive servovalve[J]. Chinese Journal Of Mechanical Engineering, 2014, 27(5):1064-1074.

[3].  Hu Xia, Jian Zhuang, De hong Yu. Multi-objective Unsupervised Feature Selection Algorithm Utilizing Redundancy Measure and Negative Epsilon-dominance for Fault Diagnosis[J]. Neurocomputing. 2014,146: 113–124. (SCI,WOS:000342529500011, JCR分区 Q2，IF=3.241)

[4].  Zhang J H, Zhuang J, Du H F, Wang S A. Self-organizing Genetic Algorithm based tuning of PID controllers [J]. Information Science, 2007

[5]. 夏虎, 庄健, 周璠, 于德弘, 采用多目标进化模型的无监督故障特征选择算法[J]. 振动与冲击, 2013.

[6]. 夏虎, 庄健, 于德弘, 面向高维特征故障数据的进化软子空间聚类算法[J].  西安交通大学学报, 2013.47(5): pp. 115-120.

[7]. 俞珏, 庄健, 于德弘. 模糊抗饱和补偿器的设计及应用[J]. 西安交通大学学报. 2013,47(1): 68-73.

[8]. 栗茂林, 梁霖, 王孙安, 庄健. 基于连续小波系数非线性流形学习的冲击特征提取方法[J]. 振动与冲击, 2012,31(1):106-111,126.

[9]. 王冠伟, 张春霞, 庄健, 于德弘, 流形学习在机械故障诊断中的应用研究[J]. 工程数学学报. 2012, 29(4): 593-599.

[10]. 张育林, 庄健, 王娜, 王孙安. 一种自适应局部线性嵌入与谱聚类融合的故障诊断方法[J]. 西安交通大学学报, 2010,(10):77-82.

[11]. 栗茂林, 杜海峰, 庄健, 王孙安. 基于模块性准则函数的模糊免疫网络聚类算法及其在故障诊断中的应用[J]. 机械工程学报, 2010,(9 ):100-106.

[12]. 张育林, 庄健, 李小虎, 王孙安. 小世界邻域优化的局部线性嵌入算法[J]. 西安交通大学学报, 2018,(12)1486-1489.

[13]. 王娜, 杜海峰, 庄健, 王孙安, 用于故障诊断的网络分割谱聚类方法. 机械工程学报, 2008(10): 228-233.

[14]. 杜海峰, 庄健, 张进华, 王孙安. 用于函数优化的小世界优化算法[J]. 西安交通大学学报 2005,39(9):1011-1015.

[15]. 庄健, 王孙安. 直流速度伺服电机的自适应遗传算法优化PID控制[J]. 机床与液压, 2003,4：95-108

[会议文章]

[1]. YU Jue, ZHUANG Jian, and YU Dehong, "Parameter optimization of PID controller based on Complex System Genetic Algorithm in Electro-hydraulic Servo Control System," presented at the Intelligent Control and Automation (WCICA), 2012 10th World Congress on Beijing, 2012.

[2]. Zhang Yulin, Zhuang Jian, Wang Sun'an. Fusion of manifold learning and spectral clustering algorithm with applications to fault diagnosis[C].2010 The 2nd International Conference on Machine Learning and Computing, ICMLC2010, pp 155-160, 2010/2/9.

[3]. Li Gang, Zhuang Jian, Hou Hongning, Yu Dehong. A LTSA and artificial immune based early fault detection for reciprocating Compressor[C].2010 8th World Congress on Intelligent Control and Automation, WCICA 2010,pp5632-5635, 2010/7/7.

[4]. Guan-Wei Wang, Jian Zhuang, De-Hong Yu. Research and application of manifold learning to fault diagnosis of reciprocating compressor[C]. in Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on. 2010. Yantai, China.

[5]. Yulin Zhang, Jian Zhuang, Sun'an Wang, Xiaohu Li. Local linear embedding in dimensionality reduction based on small world principle[C]. International Conference on Computer Science and Software Engineering, CSSE 2008, pp394-398, 2008/12/12.

[6]. Yuan M X, Wang S A, Zhuang J et al. Immune Network Algorithm Based on Improved APF for On-Line Dynamic Planning[C]. IEEE International Conference on Robotics,Automation and Mechatronics,2008：193-198. [J]

[7]. Baojuan Huang, Jian Zhuang, Dehong Yu.The self-organization genetic algorithm based on the mutation with cycle Probabilities[C].Industrial Informatics, 2008. INDIN 2008. 6th IEEE International Conference on, pp1013-1018,2008.

[8]. Zhang J H, Zhuang J, Du H F, Wang S A. PID controller optimization based on the self-organization Genetic Algorithm with cyclic mutation(A). Accepted by 6th Mexican International Conference on Artificial Intelligence(C). IEEE CS Press, Mexico: 2007. 

[9]. Du Haifeng, Zhuang Jian, Zhang Jinhua, Li Maolin, Gou Shining, Wang Sun’an，Fault diagnose of the reciprocating compressor based on artificial immune regulation network, The 7th International Conference on Frontiers of Design and Manufacturing, June, 2006, Guangzhou, vol.2:109~112

[10]. Wang Bin, Wang Sun-An, Zhuang Jian. A distributed immune algorithm for learning experience in complex industrial process control[A]. International Conference on Machine Learning and Cybernetics[C]. 2003, 2138-2141.