A simplified linguistic information feedback-based dynamical fuzzy system

Neural Computing and ApplicationsPublished 16 March 2010

Xiao Gao, S.J. Ovaska, X. Wang

DOI10.1007/s00521-010-0354-z

Source URL

Citations1

SJR quartileQ1

SJR score1.10

SNIP1.61

TL;DR

Simulation results illustrate that with a compact structure, the S-LIFDFS can still retain the advantage of inherent dynamics of linguistic information feedback and is, therefore, well suited for handling temporal problems like prediction, modeling, and control.

Abstract

Inspired by the linguistic information feedback-based dynamical fuzzy system (LIFDFS) recently proposed by the authors, we present a simplified LIFDFS (S-LIFDFS) model in this paper, which has a simpler linguistic information feedback structure. Compared with the LIFDFS, the S-LIFDFS can offer us with a considerably reduced computational complexity. We first give a detailed description of its underlying principle. Based on the gradient descent method, an adaptive learning algorithm for the feedback parameters is next derived. We also discuss the application of this S-LIFDFS in time series prediction. Three evaluation examples including prediction of two artificial time sequences and the well-known Box–Jenkins gas furnace data are demonstrated here. Simulation results illustrate that with a compact structure, our S-LIFDFS can still retain the advantage of inherent dynamics of linguistic information feedback and is, therefore, well suited for handling temporal problems like prediction, modeling, and control.

Keywords

Computer ScienceBiochemistry, Genetics and Molecular Biology

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