Advertisement

Recent Progress in Applications of Complex-Valued Neural Networks

  • Akira Hirose
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6114)

Abstract

In this keynote speech, we present recent progress in the complex-valued neural networks by focusing on their applications.

Keywords

Neural Network IEEE Transaction Ground Penetrating Radar Independent Component Analysis Independent Component Analysis Algorithm 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aizenberg, I., Paliy, D.V., Zurada, J.M., Astola, J.T.: Blur identification by multilayer neural network based on multivalued neurons. IEEE Transactions on Neural Networks 19(5), 883–898 (2008)CrossRefGoogle Scholar
  2. 2.
    Birx, D.L., Pipenberg, S.J.: A complex mapping network for phase sensitive classification. IEEE Transactions on Neural Networks 4(1), 127–135 (1993)CrossRefGoogle Scholar
  3. 3.
    Georgiou, G.M., Koutsougeras, C.: Complex domain backpropagation. IEEE Transactions on Circuits and Systems II 39(5), 330–334 (1992)zbMATHCrossRefGoogle Scholar
  4. 4.
    Goh, S., Mandic, D.P.: Nonlinear adaptive prediction of complex valued nonstationary signals. IEEE Transactions on Signal Processing 53(5), 1827–1836 (2005)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Goh, S., Mandic, D.P.: An augmented extended kalman filter algorithm for complex-valued recurrent neural networks. Neural Computation 19(4), 1–17 (2007)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Hara, T., Hirose, A.: Plastic mine detecting radar system using complex-valued self-organizing map that deals with multiple-frequency interferometric images. Neural Networks 17(8-9), 1201–1210 (2004)CrossRefGoogle Scholar
  7. 7.
    Hara, T., Hirose, A.: Adaptive plastic-landmine visualizing radar system: effects of aperture synthesis and feature-vector dimension reduction. IEICE Transactions on Electronics E88-C(12), 2282–2288 (2005)CrossRefGoogle Scholar
  8. 8.
    Hirose, A.: Applications of complex-valued neural networks to coherent optical computing using phase-sensitive detection scheme. Information Sciences –Applications–2, 103–117 (1994)Google Scholar
  9. 9.
    Hirose, A.: Complex-Valued Neural Networks. Springer, Heidelberg (2006)zbMATHCrossRefGoogle Scholar
  10. 10.
    Hirose, A.: Complex-valued neural network. Video archive (June 14, 2009), http://sites.google.com/site/ciseducationsite/home/video-tutorials-produced-by-cis/
  11. 11.
    Hirose, A.: Complex-valued neural networks: The merits and their origins. In: Proceedings of the Internatinal Joint Conference on Neural Networks (IJCNN), Atlanta, June 14–19, pp. 1237–1244. IEEE / INNS (2009)Google Scholar
  12. 12.
    Hirose, A., Asano, Y., Hamano, T.: Developmental learning with behavioral mode tuning by carrier-frequency modulation in coherent neural networks. IEEE Transactions on Neural Networks 17(6), 1532–1543 (2006)CrossRefGoogle Scholar
  13. 13.
    Hirose, A., Eckmiller, R.: Behavior control of coherent-type neural networks by carrier-frequency modulation. IEEE Transactions on Neural Networks 7(4), 1032–1034 (1996)CrossRefGoogle Scholar
  14. 14.
    Hirose, A., Eckmiller, R.: Coherent optical neural networks that have optical-frequency-controlled behavior and generalization ability in the frequency domain. Applied Optics 35(5), 836–843 (1996)CrossRefGoogle Scholar
  15. 15.
    Hirose, A., Higo, T., Tanizawa, K.: Efficient generation of holographic movies with frame interpolation using a coherent neural network. IEICE Electronics Express 3(19), 417–423 (2006)CrossRefGoogle Scholar
  16. 16.
    Kawata, S., Hirose, A.: Frequency-multiplexing ability of complex-valued Hebbian learning in logic gates. International Journal of Neural Systems 12(1), 43–51 (2008)Google Scholar
  17. 17.
    Lee, D.L.: Improvements of complex-valued Hopfield associative memory by using generalized projection rules. IEEE Transactions on Neural Networks 17(5), 1341–1347 (2006)CrossRefGoogle Scholar
  18. 18.
    Li, H., Adali, T.: A class of complex ICA algorithms based on the kurtosis cost function. IEEE Transactions on Neural Networks 19(3), 408–420 (2008)CrossRefGoogle Scholar
  19. 19.
    Masuyama, S., Hirose, A.: Walled LTSA array for rapid, high spatial resolution, and phase sensitive imaging to visualize plastic landmines. IEEE Transactions on Geoscience and Remote Sensing 45(8), 2536–2543 (2007)CrossRefGoogle Scholar
  20. 20.
    Masuyama, S., Yasuda, K., Hirose, A.: Multiple mode selection of walled-ltsa array elements for high resolution imaging to visualize antipersonnel plastic landmines. IEEE Geoscience and Remote Sensing Letters 5(4), 745–749 (2008)CrossRefGoogle Scholar
  21. 21.
    Nakano, Y., Hirose, A.: Improvement of plastic landmine visualization performance by use of ring-csom and frequency-domain local correlation. IEICE Transactions on Electronics E92-C(1), 102–108 (2009)CrossRefGoogle Scholar
  22. 22.
    Novey, M., Adali, T.: Complex ICA by negentropy maximization. IEEE Transactions on Neural Networks 19(4), 596–609 (2008)CrossRefGoogle Scholar
  23. 23.
    Sawada, H., Mukai, R., Araki, S., Makino, S.: Polar coordinate based nonlinear function for frequency-domain blind source separation. IEICE Transactions on Fundamentals of Electronics, Communications, and Computer Sciences E86A, 590–596 (2003)Google Scholar
  24. 24.
    Suksmono, A.B., Hirose, A.: Adaptive complex-amplitude texture classifier that deals with both height and reflectance for interferometric sar images. IEICE Transaction on Electronics E83-C(12), 1905–1911 (2000)Google Scholar
  25. 25.
    Suksmono, A.B., Hirose, A.: Beamforming of ultra-wideband pulses by a complex-valued spatio-temporal multilayer neural network. International Journal of Neural Systems 15(1), 1–7 (2005)CrossRefGoogle Scholar
  26. 26.
    Takeda, M., Kishigami, T.: Complex neural fields with a hopfield-like energy function and an analogy to optical fields generated in phase-conjugate resonators. Journal of Optical Society of America A 9(12), 2182–2191 (1992)CrossRefGoogle Scholar
  27. 27.
    Tay, C.S., Tanizawa, K., Hirose, A.: Error reduction in holographic movies using a hybrid learning method in coherent neural networks. Applied Optics 47(28), 5221–5228 (2008)CrossRefGoogle Scholar
  28. 28.
    Widrow, B., McCool, J., Ball, M.: The complex lms algorithm. Proceedings of the IEEE 63, 719–720 (1975)CrossRefGoogle Scholar
  29. 29.
    Yamaki, R., Hirose, A.: Singular unit restoration in interferograms based on complex-valued Markov random field model for phase unwrapping. IEEE Geoscience and Remote Sensing Letters 6(1), 18–22 (2009)CrossRefGoogle Scholar
  30. 30.
    Yang, C.C., Bose, N.: Landmine detection and classification with complex-valued hybrid neural network using scattering parameters dataset. IEEE Transactions on Neural Networks 16(3), 743–753 (2005)CrossRefMathSciNetGoogle Scholar
  31. 31.
    Zhang, Y., Ma, Y.: CGHA for principal component extraction in the complex domain. IEEE Transactions on Neural Networks 8(5), 1031–1036 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Akira Hirose
    • 1
  1. 1.Department of Electrical Engineering and Information SystemsThe University of TokyoJapan

Personalised recommendations