计算机科学 ›› 2015, Vol. 42 ›› Issue (2): 274-276.doi: 10.11896/j.issn.1002-137X.2015.02.057

• 人工智能 • 上一篇    下一篇

自适应抑制式模糊C-回归模型算法

郭华峰,赵建民,潘修强   

  1. 浙江工贸职业技术学院信息传媒学院 温州325003,浙江师范大学数理与信息工程学院 金华321004,浙江工贸职业技术学院信息传媒学院 温州325003
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金项目(61272468),浙江省温州市科技计划项目(G20130031),浙江省高职高专院校专业领军项目(lj2013146),温州面向欠发达地区项目(201424)资助

Adapting Suppressed Fuzzy C-regression Models Algorithm

GUO Hua-feng, ZHAO Jian-min and PAN Xiu-qiang   

  • Online:2018-11-14 Published:2018-11-14

摘要: 模糊C-回归模型算法由Hathaway和Bezdek提出,与硬C-回归模型算法相比有着稳定性强、收敛效果好的优点,但该算法也存在着收敛速度偏慢的问题。针对此问题,引入隶属度抑制思想,提出了抑制式模糊C-回归模型(S-FCRM)算法。实验表明,S-FCRM算法加快了算法的收敛速度,提供了较好的收敛效果。然而S-FCRM算法还存在着抑制因子参数选择的问题,针对这个问题,研究了抑制因子选择的自适应方法,进一步提出了自适应抑制式模糊C-回归模型(AS-FCRM)算法。实验表明,AS-FCRM算法有着较好的自适应效果,收敛速度更快,鲁棒性更好。

关键词: 模糊聚类,切换回归,抑制式,自适应

Abstract: Fuzzy C-regression models algorithm was proposed by Hathaway and Bezdek,which has the advantages of strong stability and good convergence effect comparing to the hard C-regression models algorithm.However,the algorithm also has the disadvantage of poor convergence speed.To solve this problem,the thought of membership suppression was introduced and an algorithm called suppressed fuzzy C-regression models(S-FCRM) algorithm was proposed.Experiments show that S-FCRM algorithm can speed up the convergence of the algorithm,and provide better convergence effect.However,S-FCRM algorithm also has the problem of inhibitory factor parameter selection.To solve this problem,the adaptive method of inhibitory factor selection was studied and an algorithm called adapting suppressed fuzzy C-regression (AS-FCRM) algorithm was proposed.Experiments show that AS-FCRM algorithm has good adaptive effect,faster convergence speed and better robustness.

Key words: Fuzzy clustering,Switching regression,Suppressed,Adapting

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