Evaluation method of granular performance indexes for fuzzy rule-based models

doi:10.11772/j.issn.1001-9081.2019050791

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (11): 3114-3119.DOI: 10.11772/j.issn.1001-9081.2019050791

• The 2019 China Conference on Granular Computing and Knowledge Discovery (CGCKD2019) • Previous Articles Next Articles

Evaluation method of granular performance indexes for fuzzy rule-based models

HU Xingchen¹, SHEN Yinghua², WU Keyu¹, CHENG Guangquan¹, LIU Zhong¹

1. College of Systems Engineering, National University of Defense Technology, Changsha Hunan 410073, China;
2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6R 2V4 AB, Canada

Received:2019-05-06 Revised:2019-07-05 Online:2019-09-11 Published:2019-11-10

模糊规则模型的粒度性能指标评估方法

胡星辰¹, 申映华², 吴克宇¹, 程光权¹, 刘忠¹

1. 国防科技大学系统工程学院, 长沙 410073;
2. 阿尔伯塔大学电气与计算机工程系, 加拿大埃德蒙顿 T6R 2V4

通讯作者: 胡星辰
作者简介:胡星辰(1989-),男,安徽合肥人,讲师,博士,主要研究方向:计算智能、粒计算、知识发现、数据挖掘、进化优化;申映华(1992-),男,江苏东台人,博士研究生,主要研究方向:计算智能、合作聚类、粒计算、决策方法;吴克宇(1990-),男,云南曲靖人,讲师,博士,主要研究方向:强化学习、随机动态规划、数据挖掘;程光权(1981-),男,安徽六安人,副研究员、博士,主要研究方向:复杂网络、决策支持;刘忠(1968-),男,湖南邵阳人,教授,博士,主要研究方向:指挥控制系统、信息系统、人工智能。

Abstract

Abstract: Fuzzy rule-based models are widely used in many fields. The existing performance indexes for the models are mainly numeric, which ignore the characteristic of fuzzy sets in the models. Aiming at the problem, a new method of evaluating the performance of fuzzy rule-based models was proposed, to effectively evaluate the non-numeric (granular) nature of results formed by the fuzzy models. In this method, different from the commonly used numeric performance indexes (such as Mean Squared Error (MSE)), the characteristics of information granules were used to represent the quality of granular results output by the model and this proposed index was applied for the performance optimization of the fuzzy model. The performance of information granule was quantified by two basic indexes, coverage rate (of data) and specificity (of information granule itself), and the maximization of the output quality of granularity (expressed as the product of coverage rate and specificity) was realized with the use of particle swarm optimization. Moreover, the distribution of information granules formed through fuzzy clustering was optimized. The experimental results show the effectiveness of the proposed method on the performance evaluation of fuzzy rule-based models

Key words: fuzzy rule-based model, granular computing, coverage rate, specificity, optimization, fuzzy clustering

摘要： 模糊规则模型广泛应用于许多领域，而现有的模糊规则模型主要使用基于数值形式的性能评估指标，忽略了对于模糊集合本身的评价，因此提出了一种模糊规则模型性能评估的新方法。该方法可以有效地评估模糊规则模型输出结果的非数值（粒度）性质。不同于通常使用的数值型性能指标（比如均方误差（MSE）），该方法通过信息粒的特征来表征模型输出的粒度结果的质量，并将该指标使用在模糊模型的性能优化中。信息粒性能采用（数据的）覆盖率和（信息粒自身的）特异性两个基本指标得以量化，并通过使用粒子群优化实现了粒度输出质量（表示为覆盖率和特异性的乘积）的最大化。此外，该方法还优化了模糊聚类形成的信息粒的分布。实验结果表明该指标对于模糊规则模型性能评估的有效性。

关键词: 模糊规则模型, 粒计算, 覆盖率, 特异性, 优化, 模糊聚类

CLC Number:

TP391

HU Xingchen, SHEN Yinghua, WU Keyu, CHENG Guangquan, LIU Zhong. Evaluation method of granular performance indexes for fuzzy rule-based models[J]. Journal of Computer Applications, 2019, 39(11): 3114-3119.

胡星辰, 申映华, 吴克宇, 程光权, 刘忠. 模糊规则模型的粒度性能指标评估方法[J]. 计算机应用, 2019, 39(11): 3114-3119.

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Evaluation method of granular performance indexes for fuzzy rule-based models

模糊规则模型的粒度性能指标评估方法

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