Chinese Journal of Intelligent Science and Technology ›› 2020, Vol. 2 ›› Issue (2): 179-185.doi: 10.11959/j.issn.2096-6652.202020

• Regular Papers • Previous Articles     Next Articles

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

Hongqing DU1,2,Dewang CHEN1,2,Yunhu HUANG1,2,Fenghua ZHU3,Lingxi LI4   

  1. 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)


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

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