Adaptive Fuzzy Modeling using Orthonormal Basis Functions for Network Traffic Flow Control

Abstract

In this chapter, we present some Fuzzy training algorithms, such as the Fuzzy LMS (Least Mean Squares) and Fuzzy RLS (Recursive Least Squares) predictors. We use concepts of multifractal analysis to present and validate a Fuzzy LMS predictor based on the autocorrelation function of a multifractal model. We evaluate the efficiency of these algorithms when applied to bandwidth allocation tasks. We also present adaptive predictive OBF (Orthonormal Basis Functions)-Fuzzy models. To this end, we model traffic traces using OBF functions obtained through multifractal analysis. Further, we insert these functions into OBF-Fuzzy models trained with the adaptive training algorithms. Updating the Fuzzy model parameters, we predict future values of real traffic traces. We also present a comparison of prediction performance of different adaptive Fuzzy algorithms including OBF-Fuzzy models. Finally, we verify the performance of the OBF-Fuzzy algorithms in modeling the buffer queueing in a communication network and controlling traffic flow rates.