An Extreme Learning Machine (ELM) Predictor for Electric Arc Furnaces' v-i Characteristics

Abstract

This paper presents an Extreme Learning Machine (ELM) time series prediction strategy to estimate the current and voltage behaviour of an Electric Arc Furnace (EAF). The proposed ELM predictor is designed for both long and short term predictions of the v-i characteristics of an EAF. The proposed predictor is evaluated using two real sensors' outputs collected over different time periods with a rate of 2000 samples per second, and its performance is compared against Feed-Forward Neural Networks (FFNN), Radial Basis Functions (RBF) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) algorithms. Experimental results obtained show the proposed ELM predictor to have superior speed and stability behaviour, while obtaining similar error values to comparable techniques.