제어로봇시스템학회:학술대회논문집
- 1992.10b
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- Pages.181-186
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- 1992
Piece-wise linear estimation of mechanical properties of materials with neural networks
Abstract
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 acoustoultrasonic 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 fit 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 backpropagation network which uses generalized delta rule as a learning algorithm.
Keywords