计算机工程

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基于Pareto烟花算法的模糊分类系统设计

罗勇,郭雅默,刘冲   

  1. (郑州大学 电气工程学院,郑州 450001)
  • 收稿日期:2015-10-26 出版日期:2017-02-15 发布日期:2017-02-15
  • 作者简介:罗勇(1977—),男,教授、博士,主研方向为计算机控制系统、智能仪器仪表、系统优化与决策;郭雅默、刘冲,硕士研究生。
  • 基金项目:
    河南省重点科技攻关计划项目(152102210036);河南省产学研合作项目(152107000058);河南省青年骨干教师项目(2015 GGJS-148)。

Design of Fuzzy Classification System Based on Pareto Firework Algorithm

LUO Yong,GUO Yamo,LIU Chong   

  1. (School of Electrical Engineering,Zhengzhou University,Zhengzhou 450001,China)
  • Received:2015-10-26 Online:2017-02-15 Published:2017-02-15

摘要:

为提高模糊模型的精确性,利用烟花算法并结合Pareto最优解集的概念,提出一种模糊建模方法。采用模糊聚类方式构建初始模糊模型,使用烟花算法对模型的结构和参数进行优化学习。在每次迭代运算过程中,通过快速非支配排序算法和Pareto最优解集的概念对子代进行评估和选择。对Wine数据样本集进行仿真实验,结果表明,该方法能够在保证较高分类精度的前提下,建立结构简单、易于理解的模糊分类系统。

关键词: 模糊系统, 烟花算法, 模糊聚类, 解释性, 精确性, 模式识别

Abstract: In order to increase the accuracy of the fuzzy model,a fuzzy classification model based on firework algorithm and Pareto optimal solution set is proposed.The fuzzy clustering method is applied to build the initial fuzzy model,and the structure and para meters of the model are optimized by the firework algorithm.In each iterative operation process,the concept of the fast non dominated sorting algorithm and the Pareto optimal solution set is used to evaluate and select the sub generation.Simulation results on Wine data sample set demonstrate that the proposed method can build fuzzy classification system of simple structure which is easy to understand under the premise of ensuring higher classification accuracy.

Key words: fuzzy system, firework algorithm, fuzzy clustering, interpretability, accuracy, pattern recognition

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