Adaptive sensor drift counteraction by a modular neural network

• The response properties of sensors such as electronic noses vary in time due to internal or environmental factors. Recalibration is often costly or technically infeasible, which is why algorithms aimed at addressing the sensor drift problem at the data processing level have been developed. These falls in two categories: The pre-processing approaches, such as component correction [1], try to extract the direction and amount of drift in the training data and remove the drift component during operation. Adaptive algorithms, such as the self-organizing map [2], try to counteract the drift during runtime by adjusting the network to the incoming data.We have previously suggested a modular neural network architecture as a model of cortical layer 4 [3]. Here we show how it quite well can handle the sensor drift problem in chemosensor data. It creates a distributed and redundant code suitable for a noisy and drifting environment. A feature extraction layer governed by competitive learning allows for network adaptation during runtime. In addition, training data can be utilized to create a prediction of the underlying drift to further improve the network performance. Hence, we attempt to combine the two aforementioned methodological categories into one network model.The capabilities of the proposed network are demonstrated on surrogate data as well as real-world data collected from an electronic nose.

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NATURVETENSKAP  -- Kemi (hsv//swe)

NATURAL SCIENCES  -- Chemical Sciences (hsv//eng)

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