一种改进细菌觅食算法的模糊控制规则库设计

The design of a fuzzy control rule library based on improved bacterial foraging optimization

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。

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higher controlaccuracy requirements make the establishment method of conventional fuzzy control rule library sometimes no longer applicable.Therefore,we proposed an improved bacterial foraging optimization（IBFO）algorithm on the basis of swarm intelligence algorithm to improve the establishment of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 therecognition 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.