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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Electrical Engineering and Information Science Journal of Electrical Engineering and Information Science Vol.1 No.1
- 발행연도
- 1996.3
- 수록면
- 108 - 117 (10page)
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초록· 키워드
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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 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 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.
목차
Abstract
Ⅰ. Introduction
Ⅱ. Previous Recurrent Neural Network Architectures
Ⅲ. Proposed Recurrent Neural Network
Ⅳ. Experimental Results
Ⅴ. Concluding Remarks
References
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