A neural network is applied to sensor signal processing in a novel ap proach to the determination of copper in reservoir water. The neural network performed well in determining the correct total of copper concentration in water when provided with only pH and copper(II) ion-selective electrode experimental data. This task followed an initial period of network exposure to a set of experimental data triplets, which were used as examples of the required input/output data mapping.
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