Linguistic Meaning and Numerical Precision of Adaptive Fuzzy Systems

• Main Authors: Jiun-Ming Deng, 鄧竣銘
• Other Authors: Ying Li
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/16141872223972850042

碩士 === 元智大學 === 電資與資訊工程研究所 === 86 === The distinctive feature of fuzzy inference systems is that their input-output relations can not only be described by numerical functions, but can also be described by linguistic fuzzy If-Then rules. We usually assume that the linguistic rules provide a rough description of the numerical function. But in a time series prediction experiment, we discover that the numerical function and the linguistic rules of an adaptive fuzzy system may appear to disagree with each other significantly. We thus define a new performance index : Dis to measure the discrepancy between the numerical function and the linguistic rules of fuzzy inference systems. We explain why adaptive fuzzy inference systems have a trade-off between numerical precision and linguistic meaning. Simulations are performed using Mackey-Glass Chaotic Time Series and Box & Jenkins Time Series data. We show that the linguistic meaning of fuzzy inference systems can help provide better initial values for system parameters. Performance of several parameter adjustment algorithms for adaptive fuzzy systems are compared. We also compare the performance of FBFN using normalized Gaussian input membership functions with that of RBFN using non-normalized Gaussian input membership functions.