Paper Info
Title
Techniques for optimizing model execution II: a systematic approach to linguistic fuzzy modeling based on input-output data
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.
Year
DOI
Venue
2000
10.1145/510378.510452
Winter Simulation Conference
Keywords
Field
DocType
systematic approach,mean squared,input-output data,new systematic algorithm,fuzzy reasoning mechanism,membership function,optimizing model execution,conceptually simple,linguistic fuzzy inference,linguistic fuzzy model,new algorithm,adaptive linguistic fuzzy model,fuzzy modeling
Data mining,Neuro-fuzzy,Fuzzy classification,Defuzzification,Fuzzy set operations,Computer science,Fuzzy logic,Adaptive neuro fuzzy inference system,Fuzzy number,Membership function,Linguistics
Conference
ISBN
Citations 
PageRank 
0-7803-6582-8
3
0.50
References 
Authors
8
3
Name
Order
Citations
PageRank
Hossein Salehfar132.53
Nagy Bengiamin231.18
Jun Huang3446.79