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Prediction of Robot Welding Damages with Markov Chain

机译:马尔可夫链的机器人焊接损伤预测

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摘要

Mathematical modeling is widely used by researchers in interpreting various events into mathematical language. There are several methods included in mathematical modeling including neural networks, naive bayes, monte carlo and markov. From some of these methods used in predicting future events. For example, Markov chain is used to predict hospital users, rain and population movements. So in this research, the authors try to use Markov Chain method to predict the damage that robots welding experiences in the coming year. Since there is no description of the damage that will occur, a prediction is needed to get a picture of the damage. In this study, a calibration process is also presented to get an error value between the predicted results and their actual results. The error value of the calibration process is a reference in determining the error value of predicted results for the coming year.
机译:研究人员广泛地使用数学建模将各种事件解释为数学语言。数学建模中包括几种方法,包括神经网络,朴素贝叶斯,蒙特卡洛和马尔科夫。从这些方法中的一些用于预测未来事件。例如,马尔可夫链用于预测医院用户,雨水和人口流动。因此,在这项研究中,作者尝试使用马尔可夫链方法来预测来年机器人焊接所遭受的损害。由于没有将要发生的损坏的描述,因此需要进行预测以获取损坏的图片。在这项研究中,还提出了一个校准过程,以获取预测结果与实际结果之间的误差值。校准过程的误差值是确定下一年预测结果的误差值的参考。

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