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

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.

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