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Hybrid Neural Network Model Based on Multi-layer Perceptron and Adaptive Resonance Theory

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

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Abstract

The model of the hybrid neural network is considered. This model consists of model ART-2 for clustering and perceptron for preprocessing of images. The perceptron provides invariant recognition of objects. This model can be used in mobile robots for recognition of new objects or scenes in sight the robot during his movement.

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References

  1. Carpenter, G.A., Grossberg, S.: Pattern Recognition by Self-Organizing Neural Networks. MIT Press, Cambridge (1991)

    Google Scholar 

  2. Lee, S.J., Hou, C.L.: An ART-Based Construction of RBF Networks. IEEE Trans. Neural Networks 13(6), 1308–1321 (2002)

    Article  Google Scholar 

  3. Baxter, R.A.: Supervised Adaptive Resonance Networks. In: Proceedings of the Conference on Analysis of Neural Network Applications, pp. 123–137. ACM, New York (1991)

    Chapter  Google Scholar 

  4. Wang, J.H., Rau, J.D., Liu, W.J.: Two-Stage Clustering Via Neural Networks. IEEE Trans. Neural Networks 14(3), 606–615 (2003)

    Article  Google Scholar 

  5. Lee, H.M., Chen, C.M., Lu, Y.F.: A Self-Organizing HCMAC Neural-Network Classifier. IEEE Trans. Neural Networks 14(1), 15–27 (2003)

    Article  Google Scholar 

  6. Gavrilov, A.V., Gubarev, V.V., Jo, K.-H., Lee, H.-H.: Hybrid Neural-Based Control System for Mobile Robot. In: Proceedings of 8th Korea-Russia International Symposium on Science and Technology KORUS-2004, TPU, Tomsk, vol. 1, pp. 31–35 (2004)

    Google Scholar 

  7. Rumelhart, D.E.: Parallel Distributed Processing. In: Mcclelland, J.L. (ed.) Explorations in the Microstructure of Cognition, vol. I, II. MIT Press, Cambridge (1986)

    Google Scholar 

  8. Gavrilov, A.V.: Hybrid Neural Network Based on Models Multi-Layer Perceptron and Adaptive Resonance Theory. In: Proceedings of 9th Korean-Russian International Symposium on Science and Technology KORUS-2005, NSTU, Novosibirsk, pp. 119–122 (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Kyung Hee University, 1, Soechen-ri, Giheung-eop, Yongin-shi, Gyeonggi-do, 449-701, Korea

    Andrey Gavrilov, Young-Koo Lee & Sungyoung Lee

Authors
  1. Andrey Gavrilov
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  2. Young-Koo Lee
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  3. Sungyoung Lee
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Gavrilov, A., Lee, YK., Lee, S. (2006). Hybrid Neural Network Model Based on Multi-layer Perceptron and Adaptive Resonance Theory. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_104

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  • DOI: https://doi.org/10.1007/11759966_104

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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