Abstract
The purpose of this study is to use a proposed neural network-based algorithm to explore the determination of the recommended measuring points for a rule surface. The task of measuring a rule surface starts from the rule surface design blueprint. Mesh grid data on the designed rule surface were selected. The pattern recognition capability of the back-propagation neural network is explored in this article. The network learning was successfully performed by the learning and testing of the network, the support of a designated acceptable perpendicular error value, a learning model in which training examples were gradually added and the adjustment of the number of training examples according to the network structure.
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