Local model networks with modified parabolic membership functions

Oliver Bänfer, Oliver Nelles, Josef Kainz, Johannes Beer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009
Pages179-183
Number of pages5
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009 - Shanghai, China
Duration: 7 Nov 20098 Nov 2009

Publication series

Name2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009
Volume1

Conference

Conference2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009
Country/TerritoryChina
CityShanghai
Period7/11/098/11/09

Fingerprint

Dive into the research topics of 'Local model networks with modified parabolic membership functions'. Together they form a unique fingerprint.

Cite this