Dynamic reconstruction of nonlinear v-i characteristic in electric arc furnaces using adaptive neuro-fuzzy rule-based networks

Authors:
A. Sadeghian;J. D. Lavers
Affiliations:
Dept. of Computer Science, Ryerson University, 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada;Dept. of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
Venue:
Applied Soft Computing
Year:
2011

Citing 6
Cited 0

Fuzzy sets in approximate reasoning, part 1: inference with possibility distributions

Fuzzy Sets and Systems - Special memorial volume on foundations of fuzzy reasoning
Nonlinear black-box modeling in system identification: a unified overview

Automatica (Journal of IFAC) - Special issue on trends in system identification
Nonlinear black-box models in system identification: mathematical foundations

Automatica (Journal of IFAC) - Special issue on trends in system identification
Industrial Applications of Fuzzy Logic and Intelligent Systems

Industrial Applications of Fuzzy Logic and Intelligent Systems
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning

IEEE Transactions on Neural Networks
Functional equivalence between radial basis function networks and fuzzy inference systems

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an application of adaptive neuro-fuzzy networks which dynamically reconstructs the model of nonlinear v-i characteristic in electric arc furnaces. Electric arc furnaces represent complex, multi-variable processes with time-variant parameters, and their effective modeling is a challenging task. This paper shows that adaptive neuro-fuzzy networks lend themselves well to nonlinear black-box modeling of v-i behavior of electric arc furnaces. A successful implementation is described, and its performance is illustrated in comparison to measurements from an operational furnace.