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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 1992.10
- 수록면
- 181 - 186 (6page)
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Many real-world problems are concerned with estimation rather than classification. This paper presents an adaptive technique to estimate the mechanical properties of materials from acousto-ultrasonic waveforms. This is done by adapting a piece-wise linear approximation technique to a multi-layered neural network architecture.
The piece-wise linear approximation network(PWLAN) finds a set of connected hyperplanes that tit all input vectors as close as possible. A corresponding architecture requires only one hidden layer to estimate any curve as an output pattern. A learning rule for PWLAN is developed and applied to the acousto-ultrasonic data. The efficiency of the PWLAN is compared with that of classical back-propagation network which uses generalized delta rule as a learning algorithm.
The piece-wise linear approximation network(PWLAN) finds a set of connected hyperplanes that tit all input vectors as close as possible. A corresponding architecture requires only one hidden layer to estimate any curve as an output pattern. A learning rule for PWLAN is developed and applied to the acousto-ultrasonic data. The efficiency of the PWLAN is compared with that of classical back-propagation network which uses generalized delta rule as a learning algorithm.
목차
ABSTRACT
OVERVIEW
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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