A new recurrent neural network architecture for pattern recognition

Hee Heon Song, Sun Mee Kang, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper, we propose a new type of recurrent neural network architecture in which each output unit is connected with itself and fully-connected with other output units and all hidden units. The proposed recurrent neural network differs from Jordan's and Elman's recurrent neural networks in view of functions and architectures because it was originally extended from the multilayer feedforward neural network for improving the discrimination and generalization power. We also prove the convergence property of learning algorithm in the proposed recurrent neural network and analyze the performance of the proposed recurrent neural network by performing recognition experiments with the totally unconstrained handwritten numeral database of Concordia University of Canada. Experimental results confirmed that the proposed recurrent neural network improves the discrimination and generalization power in recognizing spatial patterns.

Original languageEnglish
Title of host publicationTrack D
Subtitle of host publicationParallel and Connectionist Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages718-722
Number of pages5
ISBN (Print)081867282X, 9780818672828
DOIs
Publication statusPublished - 1996
Event13th International Conference on Pattern Recognition, ICPR 1996 - Vienna, Austria
Duration: 1996 Aug 251996 Aug 29

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume4
ISSN (Print)1051-4651

Other

Other13th International Conference on Pattern Recognition, ICPR 1996
CountryAustria
CityVienna
Period96/8/2596/8/29

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Song, H. H., Kang, S. M., & Lee, S. W. (1996). A new recurrent neural network architecture for pattern recognition. In Track D: Parallel and Connectionist Systems (pp. 718-722). [547658] (Proceedings - International Conference on Pattern Recognition; Vol. 4). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.1996.547658