Title :
Neuro-fuzzy predictors for the approximate prediction of v-i characteristic of electric arc furnaces
Author :
Sadeghian, A.R. ; Lavers, J.D.
Author_Institution :
Sch. of Comput. Sci., Toronto Univ., Ont., Canada
Abstract :
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
Keywords :
adaptive systems; arc furnaces; electrical engineering computing; feedforward neural nets; fuzzy neural nets; adaptive neuro-fuzzy networks; approximate v-i characteristic prediction; electric arc furnaces; feedforward neuro-fuzzy networks; long-term prediction; neuro-fuzzy predictors; nonlinear multi-variable complex systems; recorded data; single-step/multi-step prediction; Adaptive systems; Feedforward neural networks; Furnaces; Fuzzy logic; Fuzzy neural networks; Inference algorithms; Inference mechanisms; Neural networks; Steel; Voltage fluctuations;
Conference_Titel :
Fuzzy Information Processing Society, 2000. NAFIPS. 19th International Conference of the North American
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-6274-8
DOI :
10.1109/NAFIPS.2000.877416
