Title :
Application of feedforward neuro-fuzzy networks for current prediction in electric arc furnaces
Author :
Sadeghian, A.R. ; Lavers, J.D.
Author_Institution :
Dept. of Math. Phys. & Comput. Sci., Ryerson Polytech. Inst., Toronto, Ont., Canada
Abstract :
Presents the application of a class of hybrid neuro-fuzzy networks to the solution of a particular complex problem. The primary objectives are both to investigate the capability of adaptive neuro-fuzzy networks and to justify their application to predict the v-i characteristics of nonlinear, multi-variable, complex systems such as electric arc furnaces. The novelty of the work is proposing a feedforward neuro-fuzzy structure 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 :
arc furnaces; feedforward neural nets; forecasting theory; fuzzy neural nets; learning (artificial intelligence); adaptive neuro-fuzzy networks; current prediction; electric arc furnaces; feedforward neuro-fuzzy networks; feedforward neuro-fuzzy structure; long-term prediction; nonlinear multi-variable complex systems; v-i characteristics; Adaptive systems; Application software; Furnaces; Fuzzy logic; Fuzzy neural networks; Inference algorithms; Intelligent networks; Neural networks; Steel; Voltage fluctuations;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.860808
