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Extended Random Neural Networks

  • G. Martinelli
  • F. M. Frattale Mascioli
  • M. Panella
  • A. Rizzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2486)

Abstract

Random neural networks mimic at a very deep level the biological nervous system. However, it is difficult to meet during learning the biological constraints imposed on their parameters. In the paper two possible extensions are proposed in order to remove this difficulty. Moreover, the proposed learning algorithm is tailored to the specific architecture in order to reduce the computational cost. Two architectures are considered and illustrated by simulation tests.

Keywords

Bimodal neuron Recurrent architecture 

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References

  1. 1.
    Gerstner, W., van Hemmen, J.L.: Coding and information processing in neural networks. In: Domany, E., van Hemmen, J.L., Schulten, K. (eds.): Models of Neural Networks II. Springer-Verlag, Berlin Heidelberg New York (1994) 1–118Google Scholar
  2. 2.
    Gelembe, E.: Random neural networks with negative and positive signals and product form solution. Neural Computation, 1(4) (1989) 502–511CrossRefGoogle Scholar
  3. 3.
    Gelembe, E.: Stability of the random neural network model. Neural Computation, 2(2) (1990) 239–247CrossRefGoogle Scholar
  4. 4.
    Gelembe, E.: Learning in the recurrent random neural network. Neural Computation, 5(1) (1993) 154–164CrossRefGoogle Scholar
  5. 5.
    Gelembe, E., Stafylopatis, A., Likas, A.: Associative memory operation of the random network model. In Proceedings Int. Conf. Artificial Neural Networks. Helsinki, Finland (1991) 307–312Google Scholar
  6. 6.
    Gelembe, E., Koubi, V., Pekergin, F.: Dynamical random neural network approach to the traveling salesman problem. In Proceedings IEEE Symp. Sist., Man, Cybern. (1993) 630–635Google Scholar
  7. 7.
    Ghanwani, A.: A qualitative comparison of neural network models applied to the vertex covering problem. Elektrik, 2(1) (1994) 11–18Google Scholar
  8. 8.
    Gelembe, E., Kramer, C., Sungur, M., Gelembe, P.: Traffic and video quality in adaptive neural video compression. Multimedia Syst., 4 (1996) 357–369CrossRefGoogle Scholar
  9. 9.
    Cramer, C., Gelembe, E., Bakircioglu, H.: Low bit rate video compression with neural networks and temporal subsampling. Proceedings IEEE, 84(10) (1996) 1529–1543CrossRefGoogle Scholar
  10. 10.
    Gelembe, E., Feng, Y., Krishnan, K.R.: Neural network methods for volumetric magnetic resonance imaging of the human brain. Proceedings IEEE, 84(10) (1996) 1488–1496CrossRefGoogle Scholar
  11. 11.
    Gelembe, E., Ghanwani, A., Srinivasan, V.: Improved neural heuristics for multicast routing. IEEE Journal Selected Areas Commun., 15 (1997) 147–155CrossRefGoogle Scholar
  12. 12.
    Gelembe, E., Seref, E., Zhiguang, Xu: Simulation with learning agents. Proceedings IEEE, 89(2) (2001) 148–157CrossRefGoogle Scholar
  13. 13.
    Gelembe, E., Zhi-Hong, Mao, Yan-Da, Li: Function approximation with spiked random networks. IEEE Trans. Neural Networks, 10(1) (1999) 3–9CrossRefGoogle Scholar
  14. 14.
    Sugeno, M., Yasukawa, T.: A fuzzy-logic-based approach to qualitative modeling. Fuzzy Systems, 1(1) (1993) 7–31CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • G. Martinelli
    • 1
  • F. M. Frattale Mascioli
    • 1
  • M. Panella
    • 1
  • A. Rizzi
    • 1
  1. 1.INFO-COM Dpt.University of Rome ”La Sapienza”RomeItaly

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