Digital Library

of the European Council for Modelling and Simulation



Classification Of Machine Operations Based On Growing Neural Models And Fuzzy Decision


Gancho Vachkov

Published in:


ECMS 2007 Proceedings

Edited by: Ivan Zelinka, Zuzana Oplatkova, Alessandra Orsoni


ISBN: 978-0-9553018-2-7

Doi: 10.7148/2007


21st European Conference on Modelling and Simulation,

Prague, June 4-6, 2007


Citation format:

Vachkov, G. (2007). Classification Of Machine Operations Based On Growing Neural Models And Fuzzy Decision. ECMS 2007 Proceedings edited by: I. Zelinka, Z. Oplatkova, A. Orsoni (pp. 68-73). European Council for Modeling and Simulation. doi:10.7148/2007-0068.



In this paper, a novel approach to analysis and classification of complex machine operations is presented. The available data sets from different machine operations are first compressed and saved in the form of neural models that are called compressed information models (CIM). Here an original algorithm for unsupervised learning is proposed. It creates the so called growing neural models in a sense that the number of neurons is gradually increasing (growing) during the learning process, until predetermined model accuracy (the “average minimum distance”) is satisfied. The proposed algorithm has much faster convergence compared with the classical neural-gas learning that uses preliminary fixed number of neurons.

A special Knowledge Base classification scheme is also proposed in the paper. It uses a fuzzy decision block for computing the difference degree between each CIM in the Knowledge Base with the CIM of the current machine operation. The fuzzy inference procedure uses two parameters for comparison the CIMs, namely the decision the Center-of-Gravity and the General Size of the CIM.

An example for classification of 45 specially generated operations from a diesel engine of a hydraulic excavator is used to demonstrate the whole proposed technology and its applicability. This   fuzzy classification scheme is also able to discover new operations that significantly differ from all previously known operations.

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