The paper presents a new method for correction of dynamic errors by means of Artificial Neural Networks (ANNs), in which an inverse dynamic model of the sensor is realised by a neural corrector. Feedforward multilayer ANNs and a moving window method were applied. The proposed correction technique has been evaluated experimentally for small platinum RTD immersed in oil. The obtained results were compared to classical, linear correction method. Different multilayer perceptron networks were applied as neural correctors. The ANN approach significantly improved correction performance for the sensor exhibiting non-linear behaviour.
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