Abstract
In this paper, a novel soft computing modeling of adaptive neuro-fuzzy inference system in longitudinal vehicle control and warning systems is investigated. Fuzzy inference system on the outputs of neuro-fuzzy classifiers is proposed, making decision of whether the current activity is normal or intrusive. Finally, a genetic algorithm which optimizes the structure of the fuzzy decision engine is constructed. A simulation is undertaken to performance of the proposed method and results are shown promising.
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© 2012 Springer-Verlag Berlin Heidelberg
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Liu, G., Liu, Mj., Liu, Yj. (2012). Application of Neuro-fuzzy Inference in Longitudinal Vehicle Control and Warning Systems. In: Jin, D., Lin, S. (eds) Advances in Mechanical and Electronic Engineering. Lecture Notes in Electrical Engineering, vol 176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31507-7_97
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DOI: https://doi.org/10.1007/978-3-642-31507-7_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31506-0
Online ISBN: 978-3-642-31507-7
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