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首页> 外文期刊>WSEAS Transactions on Applied and Theoretical Mechanics >Comparison of the grey theory with neural network in the rigidity prediction of linear motion guide
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Comparison of the grey theory with neural network in the rigidity prediction of linear motion guide

机译：灰色理论与神经网络在直线运动导轨刚度预测中的比较

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The purpose of this paper is to compare the prediction models constructed through neural network and grey theory, and to apply the prediction model established to study of correlation between linear motion guide rigidity under the stress of tension and compression. Strain data of tension and compression are simultaneously obtained by the computer that is linked with the Universal testing machine and translated into rigidity values through the formula of F = kδ. Through this study we can understand the differences in prediction of rigidity between neural network and grey theory. Experiment results will serve as reference for manufacturers and users, with the hope that based on fewer measurement data testing time can be reduced and the outcome can be more accurately predicted. Based on fewer measurement data, the outcome can be more accurately predicted, and that with a nondestructive test can accurately predict the rigidity of the linear motion guide. The outcome indicates that the prediction model established through neural network is superior to the prediction model established through the grey theory, and that the neural network model can accurately predict the result.
机译：本文的目的是比较通过神经网络和灰色理论构建的预测模型，并将所建立的预测模型应用于研究在拉伸和压缩应力下线性运动导轨刚度之间的相关性。拉伸和压缩的应变数据是通过与万能试验机相连的计算机同时获得的，并通过F =kδ的公式转换为刚度值。通过这项研究，我们可以了解神经网络和灰色理论在刚性预测方面的差异。实验结果将为制造商和用户提供参考，希望基于更少的测量数据可以减少测试时间，并可以更准确地预测结果。基于较少的测量数据，可以更准确地预测结果，而使用无损检测可以准确地预测线性运动导轨的刚度。结果表明，通过神经网络建立的预测模型优于通过灰色理论建立的预测模型，并且该神经网络模型可以准确地预测结果。

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