Dependability evaluation of numerical control machine based on fuzzy neural networks

Hong-bin Zhang, Zhi-xin Jia, Xi An-min
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Abstract

For realizing comprehensive and accurate dependability evaluation of the numerical control (NC) machine, a dependability evaluation model based on fuzzy neural network is proposed in this paper. The reliability indexes are taken as the evaluation indexes, and the dependability of NC machine is evaluated by the fuzzy comprehensive evaluation method. Then a dependability evaluation model based on the adaptive network based fuzzy inference system (ANFIS) is established. The reliability indexes are taken as the inputs of the model, and the fuzzy comprehensive evaluation results are taken as the outputs of the model. The hybrid arithmetic, which formed by the back propagation (BP) arithmetic and least square arithmetic, is taken as the learning arithmetic of the model. After 20 steps training, the training error of the model reduced to 1.4819×10−6, and the membership functions of the inputs are auto-adjusted. The artificial factors in the evaluation process are avoided.
基于模糊神经网络的数控机床可靠性评价
为实现数控机床可靠性的全面、准确评估,提出了一种基于模糊神经网络的可靠性评估模型。以可靠性指标作为评价指标,采用模糊综合评价法对数控机床的可靠性进行评价。然后建立了基于自适应网络模糊推理系统(ANFIS)的可靠性评价模型。将可靠性指标作为模型的输入,将模糊综合评价结果作为模型的输出。采用反向传播算法和最小二乘算法形成的混合算法作为模型的学习算法。经过20步的训练,模型的训练误差降低到1.4819×10−6,并且输入的隶属度函数是自动调整的。避免了评价过程中的人为因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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