Soft Computing

, Volume 13, Issue 12, pp 1209–1217 | Cite as

Improved pattern recognition with complex artificial immune system

Focus

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.

Keywords

Complex artificial immune system Immune response Pattern recognition Transformation recognition 

References

  1. Castinglione F, Motta S, Nicosia G (2001) Pattern recognition by primary and secondary response of an artificial immune system. Theory Biosci 120(2):93–106Google Scholar
  2. Dai HW, Tang Z, Yang Y, Tamura H (2006) Affinity based lateral interaction artificial immune system. IEICE Trans Inf Syst E89-D(4):1515–1523CrossRefGoogle Scholar
  3. Dasgupta D (1996) Using immunological principles in anomaly detection. In: Proceedings of the artificial neural networks in engineering (ANNIE96). St Louis, USA, November, pp 10–13Google Scholar
  4. Dasgupta D (1999) Immune-based intrusion detection system: a general framework. National Information Systems Security Conference (NISSC)Google Scholar
  5. Dasgupta D, Forrest S (1996) Novelty detection in time series data using ideas from immunology. In: ISCA 5th international conference on intelligent systems. Reno, Nevada, pp 19–21Google Scholar
  6. Dasgupta D, Krishna Kumar K, Wong D, Berry M (2004) Negative selection algorithm for aircraft fault detection. In: The proceedings of the third international conference, ICARIS2004 on artificial immune systemsGoogle Scholar
  7. de Castro LN (2000) Artificial immune system: part II—a summary of applications. FEEC/University Campinas, CampinasGoogle Scholar
  8. de Castro LN, Timmis J (2002) Artificial immune systems: a novel paradigm to pattern recognition. In: Corchado JM, Alonso L, Fyfe C (eds) Artificial neural networks in pattern recognition, SOCO-2002. University of Paisley, UK, pp 67–84Google Scholar
  9. Forrest S, Javornik B, Smith RE, Perelson AS (1993) Using genetic algorithms to explore pattern recognition in the immune system. Evol Comput 1(3):191–211CrossRefGoogle Scholar
  10. Glodsby R, Kindt T, Kuby J, Osborne B (2003) Immunology. W.H. Freeman, New YorkGoogle Scholar
  11. Hunt JE, Cooke DE (1996) Learning using an artificial immune system. J Netw Comput Appl 19:189–212CrossRefGoogle Scholar
  12. Kim J, Bentley P (2002) Toward an artificial immune system for network intrusion detection: an investigation of dynamic clonal selection. In: Proceedings of the 2002 congress on evolutionary computation (CEC 2002). Honolulu, Hawaii, pp 1244–1252Google Scholar
  13. Kohonen T (1990) The self-organizing map. Proc IEEE 78:1464–1480CrossRefGoogle Scholar
  14. Krishna Kumar K, Neidhoefer J (1999) Immunized adaptive critic for an autonomous aircraft control application. In: Chapter 20 in the book entitled artificial immune systems and their applications. Springer, Berlin pp 221–240Google Scholar
  15. Otsu N, Kurita T (1989) Proposal of complex autoregressive model as a shape descriptor. IEEE Trans Natl Conv Rec IEICE Jpn D-496Google Scholar
  16. Sekita I, Kurita T, Otsu N (1990) Shape reconstruction from complex autoregressive coefficients. IEEE Trans Nat Conv Rec IEICE Japan D-258Google Scholar
  17. Sekita I, Kurita T, Otsu N (1991) Complex autoregressive model and its properties. Tech rep ETL, TR-91-12Google Scholar
  18. Sekita I, Kurita T, Otsu N (1992) Complex autoregressive model for shape recognition. IEEE Trans Pattern Anal Mach Intel 14(4):Google Scholar
  19. Sun WD, Tang Z, Tamura H, Ishii M (2003) An artificial immune system architecture and its applications. IEICE Trans Fundam E86.A(7):Google Scholar
  20. Tang Z, Hebishima H, Tashima K, Ishizuka O, Tanno K (1997) An immune network based on biological immune response network and its immunity. IEICE Trans Funda J80-A(11):1940–1950Google Scholar
  21. Timmis J, Knight T (2001) Artificial immune systems: using the immune as inspiration for data mining. In: Hussein RAS, Abbass A, Newton Charles S (eds) Data mining: a heuristic approach. Idea Group Publishing, Hershey, pp 209–320Google Scholar
  22. Timmis J, Neal M, Knight T (2002) AINE: machine learning inspired by the immune system. Published in IEEE transactions on evolutionary computationGoogle Scholar
  23. U.S. Department of Health and Human Services National Institutes of Health (2003) Understanding the immune system-How it works, NIH Publication No.03-5423Google Scholar
  24. White JA, Garrett SM (2003) Improved pattern recognition with artificial clonal selection. In: Proceedings of the 2nd international conference on artificial immune systems, pp 181–193Google Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Faculty of EngineeringUniversity of ToyamaToyama-shiJapan

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