Abstract

Normalization is important for Electrical Capacitance Tomography (ECT) data due to the very small capacitance values obtained either from the physical or simulated ECT system.  Thus far, there are two commonly used normalization methods for ECT, but their suitability has not been investigated.  This paper presents the work on comparing the performances of two Multilayer Perceptron (MLP) neural networks; one trained based on ECT data normalized using the conventional equation and the other normalized using the improved equation, to recognize gas-oil flow patterns.  The correct pattern recognition percentages for both MLPs were calculated and compared.  The results showed that the MLP trained with the conventional ECT normalization equation out-performed the ones trained with the improved normalization data for the task of gas-oil pattern recognition.

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