Abstract
A method for calibrating a microwave sensor is described. The method utilizes an artificial neural network trained to infer the consistency and conductivity of pulp stock slurry from the measured output spectrum of a microwave instrument. The method is both efficient and robust for extracting the multiple parameter information from the microwave signal output
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