Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

A normalization method of input data that conserves the norm information for competitive learning neural network using inner product

  • 6 Accesses

  • 1 Citations

Abstract

Normalization of input vector is essential for a competitive learning neural network using the inner product. In this paper we propose a transformation method of input vector without losing the norm information. To conserve the norm information, an additional vector component concerning the norm is introduced besides the original normalized components of the input vector. By applying the method to Kohonen’s self-organizing feature map, its usefulness is demonstrated. We also propose an optical apparatus for its realization.

This is a preview of subscription content, log in to check access.

References

  1. 1)

    T. Lu, F.T.S. Yu and D.A. Gregory: Opt. Eng. 29 (1990).

  2. 2)

    J. Duivillier, M. Killinger, K. Heggarty, K. Yao and J.L. de Bougrenet de la Tocnaye: Appl. Opt. 33 (1994).

  3. 3)

    M. Terashima, F. Shiratani and K. Yamamoto:Proceedings of JNNS’ 94, (1994) p. 221.

  4. 4)

    T. Kohonen:Self-Organization and Associative Memory, (Springer-Verlag, Berlin, 1989) 3rd ed.

Download references

Author information

Correspondence to Mikihiko Terashima or Fumiyuki Shiratani or Takeshi Hashimoto or Kimiaki Yamamoto.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Terashima, M., Shiratani, F., Hashimoto, T. et al. A normalization method of input data that conserves the norm information for competitive learning neural network using inner product. Optical Review 3, A414–A417 (1996). https://doi.org/10.1007/BF02935947

Download citation

Key words

  • normalization method
  • competitive learning neural network
  • inner product
  • L2-norm
  • L2-norm
  • SOM