Using neural network techniques in environmental sensing and measurement systems to compensate for the effects of influence quantities

Multiple influence quantities affect environmental sensing and measurement (ESM) systems. Accounting for their variations over time promotes metrological comparability and traceability of measurement results. Using suitable data processing techniques allows identification of the main influence quantities that affect the measurement result of a given quantity and allows evaluation of the associated compensation coefficients.