Abstract
An improved functional link artificial neural networks (FLANN) is presented and applied to the dynamic modelling for sensors. Compared with the traditional FLANN method, the improved FLANN method differs greatly in the calculation of partial derivatives of the weighting parameters and the dynamic model's output because the dependence of the past dynamic model's output on the parameters is fully considered. Thus, a more accurate evaluation of the gradient and a faster iteration process can be obtained. The simulation and experimental results of the infrared temperature sensor's dynamic modelling show that the improved FLANN method has higher convergence rate and more robustness.
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