Modeling of continuous digesters using adaptive RBF neural network models

Abstract

This paper presents a method for developing adaptive nonlinear models for time varying continuous digesters, which are complex nonlinear reactors used in the pulp and paper industry. The models are based on the Radial Basis Function neural network architecture and the adaptive fuzzy means algorithm is used to adapt both the structure and the connecting… (More)

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