| 한글제목(Korean Title) |
A New Recurrent Neural Network Architecture for Pattern Recognition and Its Convergence Results |
| 영문제목(English Title) |
A New Recurrent Neural Network Architecture for Pattern Recognition and Its Convergence Results |
| 저자(Author) |
Seong-Whan Lee
Young-Joon Kim
Hee-Heon Song
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| 원문수록처(Citation) |
VOL 01 NO. 01 PP. 0108 ~ 0117 (1996. 03) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In this paper, we propose a new type of recurrent neural network architectrue in which each output unit is commencted 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 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 of the proposed recurrent neural network and analyze the performance of the proposed recurrent neural network by performing recognition experiments with the totally unconstrained handwrittedn 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. |
| 키워드(Keyword) |
Recurrent Neural Network Architecture
Pattern Recognition
Convergence Results
Neural Network and Fuzzy Systems
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| 파일첨부 |
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