Fuzzy Logic Based Time Series Prediction Algorithm Using Nearest Neighborhood Clustering

Abstract

This paper presents a fuzzy logic based prediction algorithm using nearest neighborhood clustering for the prediction of time series. The proposed algorithm has been developed by using nearest neighborhood clustering scheme and optimal fuzzy system. The presented algorithm is tested by the prediction of Mackey-Glass time series, time series obtained from Logistic Map equation, and an oil refinery data series. To the best of our knowledge, very few studies have attempted to predict Logistic Map. The simulation results show that the proposed algorithm is able to predict linear, nonlinear, and even chaotic time series to a reasonable accuracy. It performs well, even when the mathematical model of time series does not exist.

Design and Development of Experimental Based Phase Modulated Model Predictive Control for Torque Ripple Reduction of MMC Fed BLDC Motor

“…In this method, rotor position error has been reduced using a hall sensor which leads to the improve the performance of torque ripple [Ozturk, S. B. et al, 2011]. Nowadays, Current control strategy is very beneficial for different application [ Irum, M. et al, 2020;Sarkar, P. et al, 2020]. So these current control strategies have also been proposed for improving ripple current.…”