A matrix of quartz sensors with a polymer layer are used to detect the different gases. The information from the sensors are used as input to the network, which computes the gas concentrations. The conventional calibration method of sensor arrays uses steady state signals which depend on the gas concentration. This method can be time consuming when many concentrations and compositions of a multicomponent mixture are required. Good experimental design may reduce the necessary effort so that the number of calibration experiments is minimised.
Dynamic measurements can reduce the time of each calibration experiment.
In this approach a random walk through the space of the gas concentrations was chosen with each step of the walk adjusted for a short time only. The concentration space was defined by a binary mixture of n-octane and toluene at concentrations between 150 and 800 ppm. Especially the extensions of feed forward nets towards recurrent structures can be used to solve problems related to dynamic evaluations. They predict gas concentrations even if the array sensors are not in their steady state. In addition such neural networks may detect parameter drifts and adapt themselves continuously to those effects.