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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2004.4
- 수록면
- 216 - 221 (6page)
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This paper describes an optimal synthesis method of binary neural network for pattern recognition. Our objective is to minimize the number of connections and the number of neurons in hidden layer by using a Newly Expanded and Truncated Learning Algorithm (NETLA) for the multilayered neural networks. The synthesis method in NETLA uses the Expanded Sum of Product (ESP) of the boolean expressions and is based on the multilayer perceptron. It has an ability to optimize a given binary neural network in the binary space without any iterative learning as the conventional Error Back Propagation (EBP) algorithm. Furthermore, NETLA can reduce the number of the required neurons in hidden layer and the number of connections. Therefore, this learning algorithm can speed up training for the pattern recognition problems. The superiority of NETLA to other learning algorithms is demonstrated by an practical application to the approximation problem of a circular region.
목차
Abstract
1. Introduction
2. Preliminaries
3. Simulations
4. Conclusion
References
저자소개
참고문헌 (9)
참고문헌 신청
[학술지(정기간행물)] - / nov.2002 / A GA- Based Learning Algorithm for Binary Neural Networks e85-a (11) : 2544 ~ 2546
[학술지(정기간행물)] - / 2002 / Extending ETL for multi-class output : 1777 ~ 1780
[학술지(정기간행물)] - / 1994 / An Active Vision System for Robot Guidance using a Low Cost Neural Network Board. In European Robotics and Intelligent Systems Conference : 480 ~ 488
[학술지(정기간행물)] - / 1992 / Using Random Weight to rain Multilayer Network of Hard-limiting Units. IEEE Trans. Neural Networks : 202 ~ 210
[학술지(정기간행물)] - / 1989 / Back Propagation fails to separate where Perceptrons Succeed. IEEE Trans. Circuits Systems : 665 ~ 674
[학술지(정기간행물)] - / 2002 / Extending ETL for multi-class output : 1777 ~ 1780
[학술지(정기간행물)] - / 1994 / An Active Vision System for Robot Guidance using a Low Cost Neural Network Board. In European Robotics and Intelligent Systems Conference : 480 ~ 488
[학술지(정기간행물)] - / 1992 / Using Random Weight to rain Multilayer Network of Hard-limiting Units. IEEE Trans. Neural Networks : 202 ~ 210
[학술지(정기간행물)] - / 1989 / Back Propagation fails to separate where Perceptrons Succeed. IEEE Trans. Circuits Systems : 665 ~ 674
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