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Certainty factor estimation using Petri neural net for HSLA steel

机译:使用Petri神经网络确定HSLA钢的确定性因子

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摘要

An unsupervised learning technique and an associative memory have been used for encoding weights by a special type of Petri network named Petri neural net for modelling the influence of alloying elements on the final property of the high strength low alloy steel. The combined effects of alloying elements for different strengthening mechanisms is predicted when weights and threshold values are chosen on the basis of metallurgical understanding. The technique is found to be effective to create an associative memory of input-output relations in unknown data sets so that the same can be subsequently be used as a predictive tool.
机译:无监督学习技术和关联记忆已通过一种称为Petri神经网络的特殊类型的Petri网络用于权重编码,以建模合金元素对高强度低合金钢最终性能的影响。当根据冶金学原理选择重量和阈值时,可以预测合金元素对不同强化机制的综合作用。发现该技术可有效地在未知数据集中创建输入-输出关系的关联存储器,以便随后可将其用作预测工具。

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