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Soft Computing

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01.10.2009 | Focus

Improved pattern recognition with complex artificial immune system

verfasst von: Wei Wang, Shangce Gao, Zheng Tang

Erschienen in: Soft Computing | Ausgabe 12/2009

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Abstract

In this paper, we introduce the application of transformation pattern recognition based on a complex artificial immune system. The key feature of the complex artificial immune system is the introduction of complex data representation. We use complex numbers as the data representation instead of binary numbers used before, besides the weight between different layers. The complex partial autocorrelation coefficients of input antigen which are considered as the antigen presentation are calculated in major histocompatibility complex (MHC) layer of the complex artificial immune system. In the simulations, the transformation of patterns, such as translation, scale or rotation, are recognized in much higher accuracy, and it has obviously higher noise tolerance ability than traditional real artificial immune system and even the complex PARCOR model.

Vorheriger Artikel Robust stability of stochastic genetic regulatory networks with discrete and distributed delays

Nächster Artikel Second order spiking perceptrons

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Titel: Improved pattern recognition with complex artificial immune system
verfasst von: Wei Wang
Shangce Gao
Zheng Tang
Publikationsdatum: 01.10.2009
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 12/2009
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-009-0418-0

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