[1]刘经纬,赵辉,周瑞,等.规则推理与神经计算智能控制系统改进及比较[J].智能系统学报,2017,(06):823-832.[doi:10.11992/tis.201602015]
 LIU Jingwei,ZHAO Hui,ZHOU Rui,et al.Improvement and comparison research between intelligent control systems based on rule based reasoning and neural computation AI methods[J].CAAI Transactions on Intelligent Systems,2017,(06):823-832.[doi:10.11992/tis.201602015]
点击复制

规则推理与神经计算智能控制系统改进及比较(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
期数:
2017年06期
页码:
823-832
栏目:
出版日期:
2017-12-25

文章信息/Info

Title:
Improvement and comparison research between intelligent control systems based on rule based reasoning and neural computation AI methods
作者:
刘经纬123 赵辉4 周瑞1 朱敏玲3 王普5
1. 北京中医药大学 中药学院, 北京 100029;
2. 首都经济贸易大学 信息学院, 北京 100070;
3. 首都经济贸易大学 计算交通科学研究中心, 北京 100070;
4. 清华大学 信息技术研究院, 北京 100084;
5. 北京工业大学 电子信息与控制工程学院, 北京 100124
Author(s):
LIU Jingwei123 ZHAO Hui4 ZHOU Rui1 ZHU Minling3 WANG Pu5
1. School of Chinese Materia, Beijing University of Chinese Medicine, Beijing 100029, China;
2. Information College, Capital University of Economics and Business, Beijing 100070, China;
3. Computational Transportation Science Center, Capital Univers
关键词:
智能系统智能控制先进控制模糊PID小波神经网络PID
Keywords:
intelligent systemintelligent controladvanced controlfuzzy PIDwavelet neural network PID
分类号:
U621;TP273
DOI:
10.11992/tis.201602015
摘要:
针对生产生活实践中的智能系统在实施控制过程中关键参数的实时在线智能整定与优化问题与需求,实现将不同类型人工智能方法与经典的控制方法对接从而构成多种复合控制(AI-CC)方法,提出改进算法并进行理论分析与仿真对比研究。首先实现了基于规则与模糊推理机制的AI-CC方法,提出了增量式改进算法,进而提出基于小波神经网络的AI-CC方法,进一步对两类智能系统的稳定性进行理论分析,提出稳定性保证算法,最后对比研究不同类型的智能系统在智能程度与性能特征方面的差异。研究成果为该领域研究者提供了多种改进的智能控制算法及其对比参照和理论分析,为该方法在工程实践中低成本地升级并稳定可靠地应用提供可操作方案。
Abstract:
To solve problems, enable real-time online tuning, and to optimize intelligent system parameters during production and daily life usage, different artificial intelligent-classical (AI-CC) control methods and systems are proposed using a combination of different types of artificial intelligent methods and classical control methods . Algorithm improvements are made, and a theoretical analysis comparing the stability and simulation of the AI-CC methods is also implemented. This research achieves the following. Implementation of a fuzzy classical-based intelligent control, and proposal of an incremental improvement algorithm and further adaptive wavelet neural network classical-based intelligent control (AI-CC). A theoretical analysis and stability ensuring method is also proposed and a comparative study undertaken. This research provides results of different types of improved AI-CC methods and a comparative study for use in further academic research, and is expected to enable low cost upgrades and a reliable solution (theoretical guarantee method) for engineering practitioners.

参考文献/References:

[1] VOLODYMYR M, KORAY K, DAVID S. Human-level control through deep reinforcement learning [J]. Nature, 2015, 518, 529-533.
[2] RIEDMILLER M, GABEL T, HAFNER R, LANGE S. Reinforcement learning for robot soccer[J]. Robots. 2009, 27: 55-73.
[3] DIUK C, COHENA, LITTMAN, M L. An object-oriented representation for efficient reinforcement learning[J]. Mach learn, 2008, 240-247.
[4] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2016, 12(21): 436-444.
[5] HINTON G. Learning multiple layers of representation. Trends in cognitive sciences[J]. 2007, 11(5): 428-434.
[6] KRIZHEVSKY A, SUTSKEVER I, HINTON GE. Imagenet classification with deep convolutional neural networks[J]. Neural information processing systems foundation. 2012, 47: 777-780.
[7] FARABET C, COUPRIE C, NAJMAN L, LECUN Y. Learning hierarchical features for scene labeling[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(8): 1915-1929.
[8] SUSANTO-LEE R, FERNANDO T, SREERAM V. Simulation of fuzzy-modified expert PID algorithms for blood glucose control[C]//10th International Conference on Control, Automation, Robotics and Vision. Hanoi, Vietnam, 2008: 1583-1589.
[9] XUE Ping, WANG Haichao, HOU Juanjuan. Based on the fuzzy PID brushless DC motor control system design[C]//International Conference on Measurement, Information and Control. Harbin, China, 2012: 703-706.
[10] OU Kai, WANG Yaxiong, LI Zhenzhe. Feedforward fuzzy-PID control for air flow regulation of PEM fuel cell system [J]. International journal of hydrogen energy, 2015, 40(35): 11686-11695.
[11] SHI Hongbo, HUANG Chuang. A BP wavelet neural network structure for process monitoring and fault detection [C]//The Sixth World Congress on Intelligent Control and Automation. Dalian, China, 2006(2): 5675-5681.
[12] SHARIFIAN M B B; MIRLO A, TAVOOSI J, SABAHI M. Self-adaptive RBF neural network PID controller in linear elevator[C]//International Conference on Electrical Machines and Systems. Beijing, China, 2011: 1-4.
[13] 赵新华, 王璞, 陈晓红. 智能投球机器人模糊PID控制[J]. 智能系统学报, 2015, 10(3): 399-406.
ZHAO Xinhua, WANG Pu, CHEN Xiaohong. Intelligent pitching robot based on fuzzy PID control[J]. CAAI transactions on intelligent systems, 2015, 10(3): 399-406.
[14] 刘经纬, 王普, 等. 专家模糊增量式自适应的参数在线整定优化系统及方法[P]. 201110023946.6, 2012-11-07.
[15] 王普, 刘经纬, 等. 自适应小波神经网络异常检测故障诊断分类系统及方法[P]. 201110023943.2, 2012-08-30.
[16] SHARIFIAN M B B, MIRLO A, TAVOOSI J, SABAHI M. Self-adaptive RBF neural network PID controller in linear elevator[C]//International Conference on Electrical Machines and Systems. Beijing, China, 2011: 1-4.
[17] NIE Yanmin, HE Zhiqiang. Optimization of the main steam temperature PID parameters based on improve BP neural network[C]//International Conference on Simulation and Modeling Methodologies, Technologies and Applications. Rome, Italy, 2015: 113-116.
[18] CHEN Zhe, FENG Tianjin, et al. The application of wavelet neural network for time series predictionand system modeling based on multiresolution learning[C]//International Conference on System, Man and Cybernetics. Tokyo, Japan, 1999(1): 425-430.
[19] LOUSSIFI H, NOURI K, BRAIEK N B. A new efficient hybrid intelligent method for nonlinear dynamical systems identification: The Wavelet Kernel Fuzzy Neural Network[J]. Communications in Nonlinear Science and Numerical Simulation. 2016, 32: 10-30.
[20] SILVA G J, DATTA A, BHATTACHARYYA S P. New results on the synthesis of PID controllers[J]. IEEE trans on automatic sontrol. 2002, 47(2): 241-252.

相似文献/References:

[1]刘增良.关于智能系统工程科学技术体系的思考[J].智能系统学报,2009,(01):12.
 LIU Zeng-liang.Development prospects in intelligence systems engineering[J].CAAI Transactions on Intelligent Systems,2009,(06):12.
[2]徐玉如,庞永杰,甘 永,等.智能水下机器人技术展望[J].智能系统学报,2006,(01):9.
 XU Yu-ru,PANG Yong-jie,GAN Yong,et al.AUV—state-of-the-art and prospect[J].CAAI Transactions on Intelligent Systems,2006,(06):9.
[3]涂序彦.广义智能系统的概念、模型和类谱[J].智能系统学报,2006,(02):7.
 TU Xu-yan.Concept, model and kinds of generalized intelligent syste m[J].CAAI Transactions on Intelligent Systems,2006,(06):7.
[4]伞 冶,叶玉玲.粗糙集理论及其在智能系统中的应用[J].智能系统学报,2007,(02):40.
 SAN Ye,YE Yu-ling.Rough set theory and its application in the intelligent systems[J].CAAI Transactions on Intelligent Systems,2007,(06):40.
[5]龚 涛,蔡自兴,江中央,等.免疫机器人的仿生计算与控制[J].智能系统学报,2007,(05):7.
 GONG Tao,CAI Zi-xing,et al.Bioinspired computation and control of immune robots[J].CAAI Transactions on Intelligent Systems,2007,(06):7.
[6]夏 凡,王 宏.基于局部异常行为检测的欺骗识别研究[J].智能系统学报,2007,(05):12.
 XIA Fan,WANG Hong.Methodologies for deception detection based on abnormal b ehavior[J].CAAI Transactions on Intelligent Systems,2007,(06):12.
[7]孙宁,方勇纯.一类欠驱动系统的控制方法综述[J].智能系统学报,2011,(03):200.
 SUN Ning,FANG Yongchun.A review for the control of a class of underactuated systems[J].CAAI Transactions on Intelligent Systems,2011,(06):200.
[8]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,(05):429.
 QIAO Junfei,PANG Zefang,HAN Honggui.Neural network optimal control for wastewater treatment processbased on APSO[J].CAAI Transactions on Intelligent Systems,2012,(06):429.
[9]田仲富,王述洋,黄英来.基于无线传感器的嵌入式森林防火智能监测系统[J].智能系统学报,2014,(06):763.[doi:10.3969/j.issn.1673-4785.201406036]
 TIAN Zhongfu,WANG Shuyang,HUANG Yinglai.Research on embedded forest fire intelligent monitoring system based on wireless sensors[J].CAAI Transactions on Intelligent Systems,2014,(06):763.[doi:10.3969/j.issn.1673-4785.201406036]
[10]曾华琳,黄雨轩,晁飞,等.书写机器人研究综述[J].智能系统学报,2016,(1):15.[doi:10.11992/tis.201507067]
 ZENG Hualin,HUANG Yuxuan,CHAO Fei,et al.Survey of robotic calligraphy research[J].CAAI Transactions on Intelligent Systems,2016,(06):15.[doi:10.11992/tis.201507067]

备注/Memo

备注/Memo:
收稿日期:2016-02-27;改回日期:。
基金项目:国家自然科学基金项目(71371128,11402006);北京社科基金研究基地项目(16JDYJB028);北京市科技计划中央引导地方科技发展专项(Z171100004717002);首经贸学术骨干培养计划(00791754840263);首经贸研究生教学改革项目(00791754310106);北京市属高校高水平教师队伍建设支持计划高水平创新团队建设计划(00791762300501).
作者简介:刘经纬,男,1982年生,副教授,博士,主要研究方向为智能控制与智能系统。发表学术论文、专利20余篇,研究成果获国家级科技竞赛3项奖励。参加多项国家级、省部级自然科学基金项目;赵辉,男,1988年生,助理研究员,博士后,主要研究方向为人工智能轨道交通智能控制。发表学术论文10余篇;周瑞,女,1983年生,讲师,博士,主要研究方向为过程控制。主持国家基金1项,完成北京市基金2项,作为骨干成员参与国家自然科学基金3项,发表学术论文10余篇。
通讯作者:周瑞.E-mail:r.zhou@bucm.edu.cn.
更新日期/Last Update: 2018-01-03