五种去模糊方法在混沌时序预测中的比较研究  

Comparative Study on Five Defuzzification Methods in Chaotic Time Series Prediction

作　　者：王扬[1] 陈艳艳[1] 

机构地区：[1]北京工业大学交通工程北京市重点实验室,北京100124

出　　处：《计算机仿真》2013年第5期133-137,共5页Computer Simulation

基　　金：北京市教育委员会科技发展计划面上项目(KM201010005021)

摘　　要：在建立Mamdani模糊推理系统时,常用到5种去模糊方法,对混沌时间序列的预测起着重要的影响。因此,定量分析影响作用将有助于建模时选择适宜的去模糊方法,而且也为方法改进提供了有力参考。提出选取3个一维混沌映射以及实际的交通流数据,通过仿真对去模糊方法的影响作用进行了比较研究。比较结果表明不同的去模糊方法对预测结果的影响作用具有显著差距,在建立Mamdani模糊推理系统时可优先选用极大均值法。同时,实验结果也表明Mamdani模糊推理系统在对混沌时间序列预测时,具有较好的泛化能力,验证了预测模型的可行性。实验结果还表明随着Lyapunov指数的增加无论哪种去模糊方法,预测效果基本上呈现逐渐变差的态势。During the construction of a Mamdani fuzzy inference system, there are five defuzzification methods commonly employed, and those methods have significant impacts on the prediction quality when predicting chaotic time series. Therefore, to quantitatively analyze the impacts for those defuzzification methods will help determine an appropriate defuzzification mechanism when modeling and provide valuable information for the improvement of defuzzi- fication method. This paper deliberatively selected three one-dimensional chaotic maps and traffic flow data, in order to perform comparative study on those defuzzification methods through a series of simulated experiments. The experi- mental results indicate that the prediction qualities are largely influenced by the defuzzification methods, and the first attempt should be made on the defuzzification method of mean value of maximum to construct a Mamdani fuzzy infer- ence system. Meanwhile, the experiments demonstrated that a relatively good generalization was achieved, implying the Mamdani fuzzy inference system is capable of predicting chaotic time series. In addition, the results also imply that the prediction quality is negatively correlated with the Lyapunov exponent of time series, in general, for all the defuzzification methods evaluated.

关 键 词：混沌时间序列 模糊推理系统 预测 去模糊 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]