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

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。

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.