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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2010.11
- 수록면
- 294 - 302 (9page)
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초록· 키워드
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The back-propagation neural network (BPN) has been broadly applied to financial distress prediction because their excellent treatment of nonlinear data with learning capabilities. Despite the wide application of BPN, some major issues must be considered before its use, such as the network topologies, learning parameters, and normalization methods for the input and output vectors. Previous studies on bankruptcy prediction with BPN have shown, however, that many researchers are interested in how to optimize the network topologies and learning parameters to improve the network’s prediction performance. In many cases, the benefits of data normalization are overlooked.
The most representative method of normalization for BPN is linear scaling, which can reduce the dimensionality of the input space, thus helping speed up the learning phase and improve the classification performance. This method, however, has the limitation of only adjusting the scale of the original data and of not being able to relieve the complicated relationships among the data. An alternative method involves applying the fuzzy set theory to normalize the data for the neural network, because the fuzzy membership function can represent the continuous and complicated values as degree of membership values, and allows the representation of the concepts that can be regarded as falling under more than one category. In this study, a genetic algorithm (GA)-optimized nonlinear fuzzy normalization method was proposed. The polynomial-based nonlinear fuzzy membership function was used to normalize the data within the value of [0, 1]. GA was thus used to find the optimal boundary value of the fuzzy parameters. Based on the results of the experiment that was conducted, the proposed method was evaluated and compared with other methods to demonstrate its advantage.
The most representative method of normalization for BPN is linear scaling, which can reduce the dimensionality of the input space, thus helping speed up the learning phase and improve the classification performance. This method, however, has the limitation of only adjusting the scale of the original data and of not being able to relieve the complicated relationships among the data. An alternative method involves applying the fuzzy set theory to normalize the data for the neural network, because the fuzzy membership function can represent the continuous and complicated values as degree of membership values, and allows the representation of the concepts that can be regarded as falling under more than one category. In this study, a genetic algorithm (GA)-optimized nonlinear fuzzy normalization method was proposed. The polynomial-based nonlinear fuzzy membership function was used to normalize the data within the value of [0, 1]. GA was thus used to find the optimal boundary value of the fuzzy parameters. Based on the results of the experiment that was conducted, the proposed method was evaluated and compared with other methods to demonstrate its advantage.
목차
Abstract
1. Introduction
2. Related Studies
3. Experimental Design
4. Experiment and Results
5. Concluding Remarks
References
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UCI(KEPA) : I410-ECN-0101-2013-003-000516856