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

Abstract

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

CLC Number:

TP391.41，TP273

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.

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References 20

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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

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

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