A Table Lookup Scheme for Fuzzy Logic Based Model Identification Applied to Time Series Prediction

摘要

Zadeh introduced Fuzzy sets in 1965 to represent and manipulate data and information that possess nonstatistical uncertainty. Since this date, fuzzy logic has been applied to many fields such as industry, medicine, economics and so on. The reason for this rapid growth in the use of fuzzy logic worldwide is that fuzzy logic provides an appropriate mechanism to describe the static and/or dynamic behavior of complex physical systems that are difficult to yield their conventional mathematical models. We can consider a fuzzy set as a fuzzy model of human concept. In this paper, we consider fuzzy modeling as an approach to form a system model using a description language based on fuzzy logic with fuzzy predicates. This paper presents a general approach to modeling and identification of dynamic systems based on fuzzy logic. A table - lookup scheme is presented to generate fuzzy rules from numerical data. This method determines a mapping from input space to output space based on the combined fuzzy rule base using defuzzijying procedure. Application to time series prediction problem is also presented.