基于改进遗传算法与支持度的模糊系统优化建模方法

doi:10.11959/j.issn.2096-6652.202020

智能科学与技术学报 ›› 2020, Vol. 2 ›› Issue (2): 179-185.doi: 10.11959/j.issn.2096-6652.202020

基于改进遗传算法与支持度的模糊系统优化建模方法

杜宏庆^1,²,陈德旺^1,²,黄允浒^1,²,朱凤华³,李灵犀⁴

¹ 福州大学数学与计算机科学学院，福建福州 350108
² 福州大学智慧地铁福建省高校重点实验室，福建福州 350108
³ 中国科学院自动化研究所复杂系统管理与控制国家重点实验室，北京 100190
⁴ 印第安纳大学-普渡大学印第安纳波利斯分校电子与计算机工程系，美国印第安纳波利斯 46202

修回日期:2020-05-14 出版日期:2020-06-20 发布日期:2020-07-14
作者简介:杜宏庆（1996– ），男，福州大学数学与计算机科学学院硕士生，主要研究方向为模糊系统、智能计算和大数据|陈德旺（1976– ），男，博士，福州大学数学与计算机科学学院教授、博士生导师，福建省“闽江学者特聘教授”，美国加州大学伯克利分校访问学者，中国自动化学会混合智能专业委员会副主任，中国自动化学会粒计算与多尺度分析专业委员会秘书长， IEEE高级会员，IEEE Transaction on ITS编委，主要研究方向为人工智能、大数据、最优化、智慧地铁和智慧新能源等|黄允浒（1990– ），男，福州大学数学与计算机科学学院博士生，主要研究方向为智能计算、大数据与最优化|朱凤华（1976– ），男，博士，中国科学院自动化研究所复杂系统管理与控制国家重点实验室副教授，主要研究方向为人工交通系统和并行交通管理系统|李灵犀（1977- ），男，博士，印第安纳大学-普渡大学印第安纳波利斯分校电子与计算机工程系副教授，主要研究方向为复杂系统的建模、分析、控制与优化，智能交通系统、智能汽车、车辆主动安全系统与人因学
基金资助:
国家自然科学基金资助项目(61976055);福建省高校智能地铁重点实验室建设基金资助项目(53001703);福建省高校智能地铁重点实验室建设基金资助项目(50013203)

A fuzzy system optimization modeling method based on improved genetic algorithm and support degree

Hongqing DU^1,²,Dewang CHEN^1,²,Yunhu HUANG^1,²,Fenghua ZHU³,Lingxi LI⁴

¹ College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350108,China
² Key Laboratory of Intelligent Metro of Universities in Fujian Province,Fuzhou University,Fuzhou 350108,China
³ State Key Laboratory of Complex Systems Management and Control,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China
³ Department of Electrical and Computer Engineering,Indiana University-Purdue University Indianapolis,Indianapolis 46202,USA

Revised:2020-05-14 Online:2020-06-20 Published:2020-07-14
Supported by:
The National Natural Science Foundation of China(61976055)

摘要/Abstract

摘要：

模糊系统是一种可解释性强的人工智能方法，经典Wang-Mendel（WM）方法因能从数据中自动获取模糊规则，而成为一种重要的智能建模方法。但是该方法存在规则数目较多、精度不高等不足，且目前的改进方法普遍存在计算复杂、效率低等问题。为此，提出一种改进遗传算法与基于支持度的规则约简相结合的模糊系统优化建模新方法——遗传模糊系统（GFS），通过优化模糊系统的结构及隶属函数参数，由目标函数的不同组合构成GFS1、GFS2与GFS3这3种模型的具体实现算法。在标准及加噪的电能输出数据集上进行模糊建模试验，其结果表明：GFSi(i=1,2,3)模型预测精度高于WM方法且规则数更少；其抗噪能力显著优于径向基函数神经网络、反向传播神经网络；GFS3 的适应度函数评估效果最佳，因此其性能最优。提出的方法在充分发挥模糊系统可解释性、鲁棒性强优势的同时保障了预测精度，是一种很有潜力的人工智能算法。

关键词: 模糊系统, 改进遗传算法, 规则约简, 可解释性, 鲁棒性

Abstract:

Fuzzy system is a kind of artificial intelligence method with strong explanatory ability.The classical Wang-Mendel (WM) method can automatically obtain fuzzy rules from data,and it has become an important intelligent modeling method.There are many problems in this method,such as the large number of rules and the low precision.And at present,there are many problems in the improved methods,such as complex calculation and low efficiency.For this reason,a new method of fuzzy system optimization modeling based on improved genetic algorithm and rule reduction based on support degree:genetic fuzzy system (GFS) was proposed.By optimizing the structure and membership function parameters of the fuzzy system,the concrete algorithm of GFS1,GFS2 and GFS3 models were constructed by different combinations of objective functions.The results of fuzzy modeling experiments on the standard and noisy power output data sets show that:1) GFSi (i =1,2,3) model fitting accuracy is higher than WM method and the number of rules is less; 2) its anti-noise capability is significantly better than that of RBF and BP neural network; 3) the fitness function of GFS3 has the best evaluation effect,so its performance is optimal.The method proposed in this paper gives full play to the advantages of the interpretability and robustness of the fuzzy system and guarantees the accuracy at the same time.It is a potential artificial intelligence algorithm.

Key words: fuzzy system, improved genetic algorithm, rule reduction, interpretability, robustness

中图分类号:

TP391.41，TP273

杜宏庆, 陈德旺, 黄允浒, 等. 基于改进遗传算法与支持度的模糊系统优化建模方法[J]. 智能科学与技术学报, 2020, 2(2): 179-185.

Hongqing DU, Dewang CHEN, Yunhu HUANG, et al. A fuzzy system optimization modeling method based on improved genetic algorithm and support degree[J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(2): 179-185.

图/表 8

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模型	w₁	w₂	w₃
GFS1	0.167	0.333	0.5
GFS2	0.286	0.571	0.143
GFS3	0.5	0.333	0.167

算法/模型	MSE	MAE	R²
RBF	0.002 27	0.038 50	0.949 30
BP	0.002 46	0.039 90	0.945 00
WM	0.008 33	0.075 60	0.813 60
GFS1	0.006 11	0.062 20	0.863 30
GFS2	0.005 80	0.059 30	0.870 10
GFS3	0.005 80	0.058 90	0.870 30

算法/模型	MSE	MAE	R²
RBF	0.007 43	0.068 8	0.833 7
BP	0.007 74	0.071 8	0.827 0
WM	0.008 93	0.080 6	0.800 3
GFS1	0.006 93	0.064 6	0.845 0
GFS2	0.006 75	0.067 4	0.849 1
GFS3	0.006 19	0.061 5	0.861 6

基于改进遗传算法与支持度的模糊系统优化建模方法

A fuzzy system optimization modeling method based on improved genetic algorithm and support degree

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[3]	辛峻峰,张永波,伯佳更,赵博文,范世缘. 基于数据驱动的遗传算法的无人艇路径规划研究[J]. 智能科学与技术学报, 2019, 1(2): 171-180.