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Tea Classification Based on Artificial Olfaction Using Bionic Olfactory Neural Network

  • Xinling Yang
  • Jun Fu
  • Zhengguo Lou
  • Liyu Wang
  • Guang Li
  • Walter J. Freeman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

Based on the research on mechanism of biological olfactory system, we constructed a K-set, which is a novel bionic neural network. Founded on the groundwork of K0, KI and KII sets, the KIII set in the K-set hierarchy simulates the whole olfactory neural system. In contrast to the conventional artificial neural networks, the KIII set operates in nonconvergent ‘chaotic’ dynamical modes similar to the biological olfactory system. In this paper, an application of electronic nose-brain for tea classification using the KIII set is presented and its performance is evaluated in comparison with other methods.

Keywords

Sensor Array Olfactory System Electronic Nose Hebbian Learning Rule Metal Oxide Sensor 
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.

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References

  1. Dottie, R., Kashwanb, K.R., Bhuyanb, M., Hinesa, E.L., Gardner, J.W.: Electronic Nose Based Tea Quality Standardization. Neural Networks 16, 847–853 (2003)CrossRefGoogle Scholar
  2. Persaud, K., Dodd, G.: Analysis of Discrimination Mechanisms in The Mammalian Olfactory System Using A Model Nose. Nature 299, 352–355 (1982)CrossRefGoogle Scholar
  3. Freeman, W.J.: Neurodynamics. An Exploration in Mesoscopic Brain Dynamics. Springer, Heidelberg (2000)MATHGoogle Scholar
  4. Li, G., Lou, Z., Wang, L., Li, X., Freeman, W.J.: Application of Chaotic Neural Model Based on Olfactory System on Pattern Recognitions. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 378–381. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. Freeman, W.J.: Mesoscopic Neurodynamics: From Neuron to Brain. Journal of Physiology-Paris, 303–322 (1994)Google Scholar
  6. Kozma, R., Freeman, W.J.: Chaotic Resonance–Methods and Applications for Robust Classification of Noisy and Variable Patterns. Int. J. Bifurcation and Chaos 11(6), 1607–1629 (2001)CrossRefGoogle Scholar
  7. Chang, H., Freeman, W.J.: Biologically Modeled Noise Stabilizing Neurodynamics for Pattern Recognition. Int. J. of Bifurcation and Chaos 8(2), 321–345 (1998)CrossRefMATHGoogle Scholar
  8. Chang, H.J., Freeman, W.J.: Local Homeostasis Stabilizes A Model of The Olfactory System Globally in Respect to Perturbations by Input During Pattern Classification. Int. J. Bifurcation and Chaos 8(11), 2107–2123 (1998)CrossRefMATHGoogle Scholar
  9. Freeman, W.J.: Characteristics of the Synchronization of Brain Activity Imposed by Finite Conduction Velocities of Axons. Int. J. of Bifurcation and Chaos 10, 2307–2322 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xinling Yang
    • 1
  • Jun Fu
    • 1
  • Zhengguo Lou
    • 1
  • Liyu Wang
    • 2
  • Guang Li
    • 3
  • Walter J. Freeman
    • 4
  1. 1.Department of Biomedical EngineeringZhejiang UniversityHangzhouP.R. China
  2. 2.Department of Optical EngineeringZhejiang UniversityHangzhouP.R. China
  3. 3.National Laboratory of Industrial Control TechnologyZhejiang UniversityHangzhouP.R. China
  4. 4.Division of NeurobiologyUniversity of California at BerkeleyBerkeleyUSA

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