Mode-Utilizing Developmental Learning Based on Coherent Neural Networks

  • Akira Hirose
  • Yasufumi Asano
  • Toshihiko Hamano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3316)

Abstract

We propose a mode-utilizing developmental learning method. Thereby a system possesses a mode parameter and learns similar or advanced tasks incrementally by using its cumulative skill. We construct the system based on the coherent neural network where we choose its carrier frequency as the mode parameter. In this demonstration, we assume two tasks: basic and advanced. The first is to ride a bicycle as long as the system can before it falls. The second is to ride as far as possible. It is demonstrated that the system finds self-organizingly a suitable value of the mode parameter in the second task learning. The learning is performed efficiently to succeed in riding for a long distance.

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References

  1. 1.
    Omori, T., Mochizuki, A., Mizutani, K.: Emergence of symbolic behavior from brain like memory with dynamic attention. Neural Networks 12, 1157–1172 (1999)CrossRefGoogle Scholar
  2. 2.
    Omori, T., Mochizuki, A.: PATON: A model of context dependent memory access with an attention mechanism. In: Brain Processes, Theories and Models, pp. 134–143. MIT Press, Cambridge (1995)Google Scholar
  3. 3.
    Wolpert, D.M., Kawato, M.: Multiple paired fotward and inverse models for motor control. Neural Networks 11, 1317–1329 (1998)CrossRefGoogle Scholar
  4. 4.
    Hartono, P., Hashimoto, S.: Temperature switching in neural network ensemble. J. Signal Processing 4, 395–402 (2000)Google Scholar
  5. 5.
    Hirose, A., Eckmiller, R.: Coherent optical neural networks that have opticalfrequency- controlled behavior and generalization ability in the frequency domain. Appl. Opt. 35(5), 836–843 (1996)CrossRefGoogle Scholar
  6. 6.
    Hirose, A., Tabata, C., Ishimaru, D.: Coherent neural network architecture realizing a self-organizing activeness mechanism. In: Proc. of Int’l Conf. on Knowledge-based Eng. Sys. KES 2001, Osaka, September 6-8, 2001, pp. 576–580 (2001)Google Scholar
  7. 7.
    Hirose, A., Ishimaru, D.: Context-dependent behavior of coherent neural systems based on self-organizing mapping of carrier frequency values. In: Proc. of Int’l Conf. on Knowledge-based Engineering Systems KES 2002, Crema, September 16-18, 2002, pp. 638–642 (2002)Google Scholar
  8. 8.
    Kawata, S., Hirose, A.: Coherent lightwave neural network systems. In: Hirose, A. (ed.) Complex- Valued Neural Networks: Theories and Appliactions. The Series on Innovative Intelligence, World Scientific Publishing Co., Singapore (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Akira Hirose
    • 1
    • 2
  • Yasufumi Asano
    • 2
  • Toshihiko Hamano
    • 1
  1. 1.Department of Frontier InformaticsThe University of TokyoTokyoJapan
  2. 2.Department of Electronic EngineeringThe University of TokyoTokyoJapan

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