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Control chart pattern clustering using a new self-organizing spiking neural network

Pham, Duc Truong, Packianather, Michael Sylvester and Charles, E. Y. A. 2008. Control chart pattern clustering using a new self-organizing spiking neural network. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 222 (10) , pp. 1201-1211. 10.1243/09544054JEM1054

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Abstract

This paper focuses on the architecture and learning algorithm associated with using a new self-organizing delay adaptation spiking neural network model for clustering control chart patterns. This temporal coding spiking neural network model employs a Hebbian-based rule to shift the connection delays instead of the previous approaches of delay selection. Here the tuned delays compensate the differences in the input firing times of temporal patterns and enables them to coincide. The coincidence detection capability of the spiking neuron has been utilized for pattern clustering. The structure of the network is similar to that of a Kohonen self-organizing map (SOM) except that the output layer neurons are coincidence detecting spiking neurons. An input pattern is represented by the neuron that is the first to fire among all the competing spiking neurons. Clusters within the input data are identified with the location of the winning neurons and their firing times. The proposed self-organized delay adaptation spiking neural network (SODA_SNN) has been utilized to cluster control chart patterns. The trained network obtained an average clustering accuracy of 96.1 per cent on previously unseen test data. This was achieved with a network of 8 × 8 spiking neurons trained for 20 epochs containing 1000 training examples. The improvement in clustering accuracy achieved by the proposed SODA_SNN on the unseen test data was twice as much as that on the training data when compared to the SOM.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Centre for Advanced Manufacturing Systems At Cardiff (CAMSAC)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TS Manufactures
Uncontrolled Keywords: spiking neural networks; temporal coding; Hebbian learning; self-organizing map
Publisher: SAGE Publications
ISSN: 0954-4054
Last Modified: 10 Oct 2017 13:07
URI: http://orca.cf.ac.uk/id/eprint/8013

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