Neuro-fuzzy predictors for the approximate prediction of v-i characteristic of electric arc furnaces
Alireza Sadeghian, J.D. Lavers · 2002
This paper presents application of feedforward neuro-fuzzy networks for single-step/multi-step prediction of the v-i characteristic of nonlinear, multi-variable, complex systems such as electric arc furnaces. The main objective is to investigate the capability of adaptive neuro-fuzzy networks to predict the v-i characteristics of electric arc furnaces. The novelties of this work are to propose the notion of approximate prediction and to it using a feedforward neuro-fuzzy suitable for long-term prediction. Successful implementations of feedforward neuro-fuzzy predictors are described and their performances are illustrated using the results obtained from adaptive neuro-fuzzy networks and recorded data.