[1]刘 胜,? 宋 佳,?? 李高云.PSO并行优化LSSVR非线性黑箱模型辨识[J].智能系统学报,2010,5(1):51-56.
 LIU Sheng,SONG Jia,LI Gao-yun.Modeling a complex nonlinear system with particle swarm optimizationand paralleloptimized least squares support vector regression[J].CAAI Transactions on Intelligent Systems,2010,5(1):51-56.
点击复制

PSO并行优化LSSVR非线性黑箱模型辨识

参考文献/References:
[1]LUO Weilin,ZOU Zaojian. Identification of response models of ship maneuvering motion using support vector machines[J].Journal of Ship Mechanics, 2007(11): 832838. 
[2]SUYKENS J A K, VAN GESTEL T, DE BRABANTER J, et al. Least squares support vector machines[M].Singapore: World Scientific, 2002: 8092.
[3]GOETHALS I, PELCKMANS K. Identification of MIMO hammerstein models using least squares support vector machines[J]. Automatica, 2005, 41(7): 12631272.
[4]SUYKENS J A K, VANDEWALLE J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3): 293300.
[5]LIU Sheng, LI Yanyan. Application of compound controller based on fuzzy control and support vector machine on ship boilerturbine coordinated control system[C]//The 2007 IEEE International Conference on Mechatronics and Automation. Harbin, China, 2007: 97102.
?[6]刘 胜,李妍妍.自适应GASVM参数选择算法研究[J].哈尔滨工程大学学报,2007,28(4):398402.
LIU Sheng, LI Yanyan. Parameter selection algorithm for support vector machines based on adaptive genetic algorithm[J]. Journal of Harbin Engineering University, 2007, 28(4): 398402.
[7]XIA Kewen,DONG Yao,DU Hongbin. Oil layer recognition model of LSSVM based on improved PSO algorithm[J].Control and Decision, 2007(12): 13851389.
[8]LI Yonggang,GUI Weihua,YANG Chunhua,CHEN Zhisheng. A resilient particle swarm optimization algorithm [J]. Control and Decision, 2008, 23 (1): 9598.
相似文献/References:
[1]唐小勇,于 飞,潘洪悦.改进粒子群算法的潜器导航规划[J].智能系统学报,2010,5(5):443.[doi:10.3969/j.issn.1673-4785.2010.05.011]
 TANG Xiao-yong,YU Fei,PAN Hong-yue.Submersible path-planning based on an improved PSO[J].CAAI Transactions on Intelligent Systems,2010,5():443.[doi:10.3969/j.issn.1673-4785.2010.05.011]
[2]沈继红,王侃.求解旅行商问题的混合粒子群优化算法[J].智能系统学报,2012,7(2):174.
 SHEN Jihong,WANG Kan.The light ray particle swarm optimization for solving the traveling salesman problem[J].CAAI Transactions on Intelligent Systems,2012,7():174.
[3]钱晓山,阳春华.基于GEP的最小二乘支持向量机模型参数选择[J].智能系统学报,2012,7(3):225.
 QIAN Xiaoshan,YANG Chunhua.A parameter selection method of a least squares support vector machine based on gene expression programming[J].CAAI Transactions on Intelligent Systems,2012,7():225.
[4]乔俊飞,逄泽芳,韩红桂.基于改进粒子群算法的污水处理过程神经网络优化控制[J].智能系统学报,2012,7(5):429.
 QIAO Junfei,PANG Zefang,HAN Honggui.Neural network optimal control for wastewater treatment processbased on APSO[J].CAAI Transactions on Intelligent Systems,2012,7():429.
[5]拓守恒,雍龙泉.一种用于PID控制的教与学优化算法[J].智能系统学报,2014,9(6):740.[doi:10.3969/j.issn.1673-4785.201304072]
 TUO Shouheng,YONG Longquan.A modified teaching-learning-based optimization algorithm for parameter tuning of a PID controller[J].CAAI Transactions on Intelligent Systems,2014,9():740.[doi:10.3969/j.issn.1673-4785.201304072]
[6]于立君,陈佳,刘繁明,等.改进粒子群算法的PID神经网络解耦控制[J].智能系统学报,2015,10(5):699.[doi:10.11992/tis.201406028]
 YU Lijun,CHEN Jia,LIU Fanming,et al.An improved particle swarm optimization forPID neural network decoupling control[J].CAAI Transactions on Intelligent Systems,2015,10():699.[doi:10.11992/tis.201406028]
[7]翟俊海,刘博,张素芳.基于粗糙集相对分类信息熵和粒子群优化的特征选择方法[J].智能系统学报,2017,12(3):397.[doi:10.11992/tis.201705004]
 ZHAI Junhai,LIU Bo,ZHANG Sufang.A feature selection approach based on rough set relative classification information entropy and particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2017,12():397.[doi:10.11992/tis.201705004]
[8]徐鹏,谢广明,文家燕,等.事件驱动的强化学习多智能体编队控制[J].智能系统学报,2019,14(1):93.[doi:10.11992/tis.201807010]
 XU Peng,XIE Guangming,WEN Jiayan,et al.Event-triggered reinforcement learning formation control for multi-agent[J].CAAI Transactions on Intelligent Systems,2019,14():93.[doi:10.11992/tis.201807010]
[9]刘楠,刘福才,孟爱文.基于改进PSO和FCM的模糊辨识[J].智能系统学报,2019,14(2):378.[doi:10.11992/tis.201707025]
 LIU Nan,LIU Fucai,MENG Aiwen.Fuzzy identification based on improved PSO and FCM[J].CAAI Transactions on Intelligent Systems,2019,14():378.[doi:10.11992/tis.201707025]

备注/Memo

收稿日期:2009-03-01.
基金项目:
黑龙江省自然科学基金资助项目(A200419).
通信作者:刘 胜.E-mail:liu.sch@163.com.
作者简介:
刘 胜,男,1957年生,教授,博士生导师,黑龙江省教学名师,黑龙江省重点一级学科“控制科学与工程”学科负责人.兼任教育部工程研究中心“船舶控制工程研究中心”主任,中国造船学会仪器仪表学术委员会副主任,黑龙江省自动化学会副理事长.主要研究方向为智能控制、鲁棒控制、船舶航行与姿态控制.目前承担国家“973”计划项目、国防基础研究基金项目、国防预研项目4项,省部级项目6项.曾获黑龙江省优秀教学工作者,中国船舶工业总公司优秀青年科技工作者,获省部级科学技术奖7项,省教学成果奖一等奖2项、二等奖2项,省教育科学研究成果一等奖4项,省部级自然科学技术学术成果奖8项.发表学术论文150余篇,被SCI、EI、ISTP检索70余篇,出版学术著作3部.
宋 佳,女,1983年生,博士研究生,主要研究方向为智能控制、船舶姿态控制.参与科研项目2项,发表学术论文10余篇,被EI、ISTP检索6篇.
?李高云,男,1981年生,博士研究生,主要研究方向为智能控制、故障诊断与容错控制、船舶航行与姿态控制.参与科研项目3项,获黑龙江省科学技术二等奖1项,黑龙江省高校科学技术一等奖1项.发表学术论文近10篇,被EI、ISTP检索4篇.

更新日期/Last Update: 2010-03-31
Copyright © 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134 邮箱:tis@vip.sina.com