Local Model Networks with Modified Parabolic Membership Functions

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. local model networks, which provide a solution for this problem, become more important for modeling. Usually Gaussians 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 membership function for the efficient implementation of local model networks is proposed. Their advantages compared to standard local model networks and to look-up tables are illustrated by the application of an intake manifold model of a combustion engine.