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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Multilayer Perceptron Network with Modified Sigmoid Activation Functions
verfasst von : Tobias Ebert, Oliver Bänfer, Oliver Nelles
Erschienen in: Artificial Intelligence and Computational Intelligence
Verlag: Springer Berlin Heidelberg
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Models in today’s microcontrollers, e.g. engine control units, are realized with a multitude of characteristic curves and look-up tables. The increasing complexity of these models causes an exponential growth of the required calibration memory. Hence, neural networks, e.g. multilayer perceptron networks (MLP), which provide a solution for this problem, become more important for modeling. Usually sigmoid functions are used as membership functions. The calculation of the therefore necessary exponential function is very demanding on low performance microcontrollers. Thus in this paper a modified activation function for the efficient implementation of MLP networks is proposed. Their advantages compared to standard look-up tables are illustrated by the application of an intake manifold model of a combustion engine.