Clustering of Complex Electronic Systems with Self-Ordering Maps 2005-01-1286
In this paper an approach to clustering of complex electronic systems using Self-Ordering Maps (SOMs) is presented. SOMs are neural networks which learn through a competitive learning algorithm. In order to use SOMs for the clustering of electronic networks, a representation of the communication behavior in n-dimensional space is developed. The SOM is then used as a nonlinear projection of this space onto a two-dimensional plane.
Two examples of clustering are given. The more complex of the two is verified by comparing the behavior of the clustered system and the unclustered system on a simple model of the CAN bus.
It is shown that SOMs can be used to effectively cluster complex electronic systems.
Citation: Nenninger, P., Rambow, T., and Kiencke, U., "Clustering of Complex Electronic Systems with Self-Ordering Maps," SAE Technical Paper 2005-01-1286, 2005, https://doi.org/10.4271/2005-01-1286. Download Citation
Author(s):
Philipp Nenninger, Thomas Rambow, Uwe Kiencke
Affiliated:
Institute of Industrial Information Technology, Universität Karlsruhe (TH)
Pages: 8
Event:
SAE 2005 World Congress & Exhibition
ISSN:
0148-7191
e-ISSN:
2688-3627
Also in:
Distributed Embedded Systems Engineering 2005-SP-1921, SAE 2005 Transactions Journal of Passenger Cars: Electronic and Electrical Systems-V114-7
Related Topics:
Electronic control systems
Neural networks
Education and training
Mathematical models
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