Techniques for optimizing model execution II: a systematic approach to linguistic fuzzy modeling based on input-output data

Authors:
Hossein Salehfar;Nagy Bengiamin;Jun Huang
Affiliations:
University of North Dakota, Grand Forks, ND;California State University - Fresno, Fresno, CA;University of North Dakota, Grand Forks, ND
Venue:
Proceedings of the 32nd conference on Winter simulation
Year:
2000

Citing 3
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Abstract

A new systematic algorithm to build adaptive linguistic fuzzy models directly from input-output data is presented in this paper. Based on clustering and projection in the input and output spaces, significant inputs are selected, the number of clusters is determined, rules are generated automatically, and a linguistic fuzzy model is constructed. Then, using a simplified fuzzy reasoning mechanism, the Back-Propagation (BP) and Least Mean Squared (LMS) algorithms are implemented to tune the parameters of the membership functions. Compared to other algorithms, the new algorithm is both computationally and conceptually simple. The new algorithm is called the Linguistic Fuzzy Inference (LFI) model.