A Novel Artificial Immune Network Model and Analysis on Its Dynamic Behavior and Stabilities

1. Klarreich, E.: Inspired by Immunity. Nature 415(31), 468–470 (2002)

   Article  Google Scholar 

2. Jerne, N.K.: Towards a Network Theory of the Immune System. Annual Immunology 125C, 373–389 (1974)

   Google Scholar 

3. Jerne, N.K.: The Generative Grammar of the Immune System. In: Wigzell, H. (ed.) Presentation Speech to Nobel Prize in Physical or Medicine (1984), http://nobelprize.org/medicine/laureates/1984/presentation-speech.html

4. Farmer, J.D., Packard, N.H., Perelson, A.S.: The Immune System. Adaptation, and Machine Learning. Physica 22D, 187–204 (1986)

   MathSciNet  Google Scholar 

5. Chao, D.L., Davenport, M.P., Forrest, S., Perelson, A.S.: A stochastic model of cytotoxic T cell responses. Journal of Theoretical Biology 228(2), 227–240 (2004)

   Article  MathSciNet  Google Scholar 

6. Esponda, F., Forrest, S., Helman, P.: A formal framework for positive and negative detection. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 34(1), 357–373 (2004)

   Article  Google Scholar 

7. Dasgupta, D., Gonzalez, F.: An Immunity-Based Technique to Characterize Intrusions in Computer Networks. IEEE Transactions on Evolutionary Computation 6(3), 156–162 (2002)

   Google Scholar 

8. Gomez, J., Gonzalez, F., Dasgupta, D.: An Immuno-Fuzzy Approach to Anomaly Detection. In: Proceedings of IEEE International Conference on Fuzzy Systems, pp. 1219–1224 (2003)

   Google Scholar 

9. De Sousa, J.S., de Gomes, L., Bezerra, G.B., de Castro, L.N., Von Zuben, F.J.: An Immune-Evolutionary Algorithm for Multiple Rearrangements of Gene Expression Data. Genetic Programming and Evolvable Machines 5, 157–179 (2004)

   Article  Google Scholar 

10. De Castro, L.N., Von Zuben, F.J.: Learning and Optimization Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation 6(3), 239–251 (2002)

    Article  Google Scholar 

11. Timmis, J., Boggess, L., Watkins, A.: Artificial Immune Recognition System (AIRS): An Immune Inspired Supervised Machine Learning Algorithm. Genetic Programming and Evolvable Machines 5(1), 51–58 (2004)

    Google Scholar 

12. Watkins, A., Timmis, J.: Exploiting Parallelism Inherent in AIRS, an Artificial Immune Classifier. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 427–438. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

13. White, J.A., Garrett, S.M.: Improved Pattern Recognition with Artificial Clonal Selection. In: Proceedings of the 2nd International Conference on Artificial Immune Systems, pp. 181–193

    Google Scholar 

14. Ishiguro, K., Kondo, T., Watanabe, Y.: Emergent Construction of Artificial Immune Networks for Autonomous Mobile Robots. In: Proceedings of IEEE International Conference on System Man and Cybernetics, pp. 1222–1228 (1997)

    Google Scholar 

15. Tang, Z., Yamaguchi, T., Tashima, K., Ishizuka, O., Tanno, K.: Multiple-Valued Immune Network Model and Its Simulations. In: Proceedings of 27th IEEE International Symposium on Multiple-Valued Logic, pp. 233–238 (1997)

    Google Scholar 

16. Tan, X.H., Zhang, J.Y., Yang, Y.R.: Study on Global Exponential Stability of Neural Networks and Its Convergence Estimate. Journal of Electronics and Information Technology 25(10), 1361–1366 (2003)

    Google Scholar 

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