+高级检索
首页 > 过刊浏览>2017年第40卷第7期 >63-71. DOI:10.11835/j.issn.1000-582X.2017.07.009
PDF HTML阅读 XML下载 导出引用 引用提醒
一种改进细菌觅食算法的模糊控制规则库设计
作者:
中图分类号:

TP13

基金项目:

安徽省教育厅自然科学基金重点资助项目（KJ2015A058），安徽省高校自然科学基金研究资助项目（KJ2013A054）。


The design of a fuzzy control rule library based on improved bacterial foraging optimization
Author:
  • 摘要
    • | |
  • 访问统计
    • |
  • 参考文献 [15]
    • |
  • 相似文献
    • | | |
  • 文章评论
    摘要:

    模糊控制规则库的建立决定了模糊控制系统的性能，而在能源生产、机器人控制等领域中对控制精度要求越来越高，使得常规的模糊控制规则库建立方法有时不再适用。为此，提出一种新型的基于集群智能算法的改进细菌觅食算法IBFO（improved bacterial foraging optimization）来改进规则库的建立过程，剖析了依据人工经验归纳来制定模糊规则库的不完善性，描述了对改进型的模糊控制系统的构建步骤，优化了改进型TSK（takagi-suegeno-kang）模糊系统C-ATSKFS（constant-ameliorative TSK fuzzy system）规则库的高斯隶属函数参数。通过与现有的规则库建立方法相比对，可知改进算法能有效地提高模糊控制系统的识别精度。MATLAB仿真结果表明：提出的新型细菌觅食算法对模糊控制规则库的建立具有较高的实用价值。

    Abstract:

    The establishment of the fuzzy control rule library determines the performance of the fuzzy control system. And in the fields of energy production and robot control, higher and higher control-accuracy requirements make the establishment method of conventional fuzzy control rule library sometimes no longer applicable. Therefore, we proposed an improved bacterial foraging optimization (IBFO) algorithm on the basis of swarm intelligence algorithm to improve the establishment of rule library. Firstly, the imperfectness of fuzzy rule library based on artificial experience induction was analyzed. Secondly, the improved fuzzy control system was described. Finally, the Gaussian membership function parameters of the improved TSK fuzzy system (C-ATSKFS,constant-ameliorative TSK fuzzy system) rule library were optimized. Compared with the existing method, the improved algorithm can effectively increase the recognition accuracy of the fuzzy control system. MATLAB simulation results show that the proposed novel foraging algorithm has a high practical value for the establishment of fuzzy control rule library.

    参考文献
    [1] Chen H. Optimal fuzzy PID controller design for an active magnetic bearing system based on adaptive genetic algorithms[J]. Mathematical Structures in Computer Science, 2014, 24(5), 1-14.
    [2] Hsu C H, Juang C F, Jhan Y H. Evolutionary fuzzy control using rule-based multi-objective genetic algorithms[C]//International Conference on Fuzzy Theory and Its Applications. IEEE, 2013:391-396.
    [3] 李合生,毛剑琴,代冀阳.基于遗传算法的广义Takagi-Sugeno模糊逻辑系统最优参数辨识[J].自动化学报,2002,28(4):581-585. LI Hesheng, MAO Jianqin, DAI Jiyang. Generalized takagi-sugeno fuzzy logical system optimal parameter identification based on genetic algorithm[J]. Acta Automatica Sinica, 2002, 28(4):581-585. (in Chinese)
    [4] 田毅,张欣,张良,等.神经网络工况识别的混合动力电动汽车模糊控制策略[J].控制理论与应用,2011,28(3):363-369. TIAN Yi, ZHANG Xin, ZHANG Liang, et al. Fuzzy control strategy for hybrid electric vehicle based on neural network identification of driving conditions[J]. Control Theory & Applications, 2011, 28(3):363-369. (in Chinese)
    [5] 洪昭斌,陈力.基于神经网络的漂浮基空间机械臂的自学习模糊控制[J].机械科学与技术,2009,28(7):976-980. HONG Zhaobin, CHEN Li. Self-learning fuzzy control of a space manipulator based on a neural network[J]. Mechanical Science and Technology for Aerospace Engineering, 2009, 28(7):976-980. (in Chinese)
    [6] Ganji M F, Abadeh M S. Rule based classification system for medical data mining using fuzzy ant colony optimization[J]. Lecture Notes in Engineering and Computer Science, 2010, 2186(1):466.
    [7] 刘泽华,李振水,贾爱绒.免疫模糊控制算法在飞机防滑刹车系统中的应用研究[J].计算机测量与控制,2009,17(8):1563-1565. LIU Zehua, LI Zhenshui, JIA Airong. Study on aircraft anti-skid brake system based on immune fuzzy control algorithm[J]. Computer Measurement & Control, 2009, 17(8):1563-1565. (in Chinese)
    [8] Guzmán M A, Delgado A, Carvalho J D. A novel multiobjective optimization algorithm based on bacterial chemotaxis[J]. Engineering Applications of Artificial Intelligence, 2010, 23(3):292-301.
    [9] Pandi V R, Biswas A, Dasgupta S, et al. A hybrid bacterial foraging and differential evolution algorithm for congestion management[J]. European Transactions on Electrical Power, 2010, 20(7):862-871.
    [10] 严天一,闫海敬,侯兆萌,等.车辆半主动悬架粒子群模糊混合控制策略[J].农业机械学报,2013,44(8):1-7. YAN Tianyi, YAN Haijing, HOU Zhaomeng, et al. PSO-fuzzy-hybrid control strategy of semiactive suspensions[J]. Transactions of the Chinese Society for Agricultural Machinery, 2013, 44(8):1-7. (in Chinese)
    [11] Liu X D. Modified TSK fuzzy system in stock price forecasting[C]//IEEE International Conference on Information and Financial Engineering. IEEE, 2013:621-624.
    [12] Ray P K, Subudhi B. BFO optimized RLS algorithm for power system harmonics estimation[M]. Elsevier Science Publishers B. V. 2012.
    [13] 范国英,郭雷,孙勇,等.BFO-PSO混合算法的PSS参数优化设计[J].电力系统及其自动化学报,2010,22(6):28-31. FAN Guoying, GUO Lei, SUN Yong, et al. Parameters optimize of PSS based on BFO-PSO hybrid algorithm[J]. Proceedings of the Chinese Society of Universities, 2010, 22(6):28-31. (in Chinese)
    [14] 曹天问,雷秀娟.改进BFO算法在函数优化问题上的应用[J].计算机工程与应用, 2013, 49(13):175-179. CAO Tianwen, LEI Xiujuan. Application of improved bacteria foraging optimization algorithm on function optimization[J]. Computer Engineering and Applications, 2013, 49(13):175-179. (in Chinese)
    [15] Passino K M. Biomimicry of bacterial foraging for distributed optimization and control[J]. IEEE Control Systems Magazine, 200222(3):52-67.
    相似文献
    引证文献
引用本文

徐驰,冯旭刚,章家岩,李新光,曹月洋.一种改进细菌觅食算法的模糊控制规则库设计[J].重庆大学学报,2017,40(7):63-71.

复制
分享
文章指标
  • 点击次数:808
  • 下载次数: 1123
  • HTML阅读次数: 488
  • 引用次数: 0
历史
  • 收稿日期:2016-12-13
  • 在线发布日期: 2017-08-01
文章二维码
您是第11236221 位访问者
主管单位：中华人民共和国教育部
主办单位：重庆大学
地址：重庆市沙坪坝正街174号
电话：
重庆大学学报 ® 2025 版权所有