Artificial Immune Network in the Recognition of Yunnan Herbal Medicine

Abstract:

Concerning simple and unscientific of Yunnan herbal medicine identification method, a kind of identification method based on Artificial Immune Network was proposed in this paper. Using high performance liquid chromatography extracts fingerprints from Yunnan herbal medicine,and then the data of fingerprints were trained by the Artificial Immune Network. In comparison with the traditional K-means algorithm,the experiment results show that Artificial Immune Network has higher classification and recognition ability.

• Periodical: Advanced Materials Research (Volumes 718-720)
• Pages: 2353-2358
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.718-720.2353
• Online since: July 2013
• Authors: Chao Huang, Jin He, Ji Yong Zhao
• Keywords: Artificial Immune Network, Identification, K-Means Algorithm, Yunnan Herbal Medicine

References

[1] Dipankar Dasgupta. Using Immunological Principles in Anomaly Detection. In Proceedings of the Artificial Neural Networks in Engineering (ANNIE'96), St. Louis,USA, November 10-13 1996.

[2] Bezdek, J. C. & Pal, N. R. (1992), Fuzzy Models for Pattern Recognition: Methods that Search for Structures in Data, New York: IEEE Press.

[3] Steven A. Frank. The Design of Natural and Artificial Adaptive Systems. Academic Press,New York, M. R.Rose and G. V. Lauder edition, 1996.

[4] Thomas P. Caudell and David S. Newman. An Adaptive Resonance Architecture to Define Normality and Detect Novelties in Time Series and Databases. In IEEE World Congress on Neural Networks, pages IV166-176, Portland,Oregon, 3-7 July 1993.

[5] De Castro, Fernando J.An Evolutionary Immune Network for Data Clustering[C].In: Proc of the IEEE SBRN, 2000: 84~89.

[6] Alan S. Perelson. Immune network theory. Immunological Reviews, (10):5-36, 1989.