Abstract
Over the past few decades there has been a growing interest in the use of biology as a source of inspiration for solving computational problems. The motivation of this field is primarily to extract useful metaphors from natural biological systems, in order to create effective computational solutions to complex problems in a wide range of domain areas. The more notable developments have been the neural networks inspired by the working of the brain, and the evolutionary algorithms inspired by neo-Darwinian theory of evolution. This paper presents the theory of an immune network model, and it tries to apply to solve signal classification problems.
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Keywords
- Artificial Immune System
- Cluster Accuracy
- Immune Network
- Clonal Selection Algorithm
- Suppression Threshold
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
De Castro, L.N., Von Zuben, F.J.: An Evolutionary Immune Network for Data Clustering. In: Proc. of the IEEE SBRN, pp. 84–89 (2000a)
De Castro, L.N., Von Zuben, F.J.: The Clonal Selection Algorithm with Engineering Applications. In: GECCO 2000 – Workshop Proceedings, pp. 36–37 (2000b)
De Castro, L.N., Von Zuben, F.J.: Artificial Immune Systems: Part I – Basic Theory and Applications, Technical Report – RT DCA 01/99, p. 95 (1999)
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© 2004 Springer-Verlag Berlin Heidelberg
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Święcicki, M., Wajs, W., Wais, P. (2004). An Artificial Immune Algorithms Apply to Pre-processing Signals. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science - ICCS 2004. ICCS 2004. Lecture Notes in Computer Science, vol 3037. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24687-9_107
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DOI: https://doi.org/10.1007/978-3-540-24687-9_107
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