Learning temporal sequences by complex neurons with local feedback

Makoto Kinouchi, Masafumi Hagiwara

Research output: Contribution to conferencePaper

9 Citations (Scopus)

Abstract

To deal with temporal sequences is very important and difficult problem for applications of neural networks. In this paper, we propose a Multilayer Network using Complex neurons with local Feedback (MNCF). A complex neuron can keep previous information by the phase component. We derive simple learning algorithm based on the back-propagation for temporal sequences. It is shown in computer simulations that the proposed network has better ability than the conventional real ones, including Elman's network.

Original languageEnglish
Pages3165-3169
Number of pages5
Publication statusPublished - 1995 Dec 1
EventProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) - Perth, Aust
Duration: 1995 Nov 271995 Dec 1

Other

OtherProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6)
CityPerth, Aust
Period95/11/2795/12/1

ASJC Scopus subject areas

  • Software

Fingerprint Dive into the research topics of 'Learning temporal sequences by complex neurons with local feedback'. Together they form a unique fingerprint.

  • Cite this

    Kinouchi, M., & Hagiwara, M. (1995). Learning temporal sequences by complex neurons with local feedback. 3165-3169. Paper presented at Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6), Perth, Aust, .