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Proceedings Paper

Multilayer cellular neural network and fuzzy C-mean classifiers: comparison and performance analysis
Author(s): Maite Trujillo San-Martin; Vejen Hlebarov; Mustapha Sadki
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Paper Abstract

Neural Networks and Fuzzy systems are considered two of the most important artificial intelligent algorithms which provide classification capabilities obtained through different learning schemas which capture knowledge and process it according to particular rule-based algorithms. These methods are especially suited to exploit the tolerance for uncertainty and vagueness in cognitive reasoning. By applying these methods with some relevant knowledge-based rules extracted using different data analysis tools, it is possible to obtain a robust classification performance for a wide range of applications. This paper will focus on non-destructive testing quality control systems, in particular, the study of metallic structures classification according to the corrosion time using a novel cellular neural network architecture, which will be explained in detail. Additionally, we will compare these results with the ones obtained using the Fuzzy C-means clustering algorithm and analyse both classifiers according to its classification capabilities.

Paper Details

Date Published: 2 November 2004
PDF: 8 pages
Proc. SPIE 5558, Applications of Digital Image Processing XXVII, (2 November 2004); doi: 10.1117/12.561077
Show Author Affiliations
Maite Trujillo San-Martin, Brunel Univ. (United Kingdom)
Vejen Hlebarov, City Univ./London (United Kingdom)
Mustapha Sadki, Brunel Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 5558:
Applications of Digital Image Processing XXVII
Andrew G. Tescher, Editor(s)

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