A simplified linguistic information feedback-based dynamical fuzzy system (S-LIFDFS) - Part I. Theory

For pt.2 see ibid., p.51-6 (2005). This work consists of two parts: theory and evaluation. In Part I, inspired by the linguistic information feedback-based dynamical fuzzy system (LIFDFS) recently proposed by the authors, we present a simplified LIFDFS (S-LIFDFS) model, which has a simpler linguistic information feedback structure. Compared with the LIFDFS, the S-LIFDFS can offer us with the 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. Part II of this work discusses applying 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.