Linguistic Meaning and Numerical Precision of Adaptive Fuzzy Systems
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 t...
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| Format: | Others |
| Language: | zh-TW |
| Online Access: | http://ndltd.ncl.edu.tw/handle/16141872223972850042 |
| Summary: | 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.
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