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首页> 外文期刊>Proceedings of the institution of mechanical engineers >Multi-objective evolutionary optimization of polynomial neural networks for fatigue life modelling and prediction of unidirectional carbon-fibre-reinforced plastics composites
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Multi-objective evolutionary optimization of polynomial neural networks for fatigue life modelling and prediction of unidirectional carbon-fibre-reinforced plastics composites

机译：多项式神经网络的多目标进化优化，用于疲劳寿命建模和单向碳纤维增强塑料复合材料的预测

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摘要

In this article, evolutionary algorithms (EAs) are employed for multi-objective Pareto optimum design of group method data handling (GMDH) -type neural networks that have been used for fatigue life modelling and prediction of unidirectional (UD) carbon-fibre-reinforced plastics (CFRP) composites using input-output experimental data. The input parameters used for such modelling are stress ratio, cyclic strain energy, fibre orientation angle, maximum stress, and failure stress level in one cycle. In this way, EAs with a new encoding scheme are first presented for evolutionary design of the generalized GMDH-type neural networks, in which the connectivity configurations in such networks are not limited to adjacent layers. Second, multi-objective EAs with a new diversity preserving mechanism are used for Pareto optimization of such GMDH-type neural networks. The important conflicting objectives of GMDH-type neural networks that are considered in this work are training error (TE), prediction error (PE), and number of neurons (N). Different pairs of these objective functions are selected for two-objective optimization processes. Therefore, optimal Pareto fronts of such models are obtained in each case, which exhibit the trade-offs between the corresponding pair of conflicting objectives and, thus, provide different non-dominated optimal choices of GMDH-type neural network model for fatigue life of UD CFRP composites. Moreover, all the three objectives are considered in a three-objective optimization process, which consequently leads to some more non-dominated choices of GMDH-type models representing the trade-offs among the TE, PE, and N (complexity of network), simultaneously. The comparison graphs of these Pareto fronts also show that the three-objective results include those of the two-objective results and, thus, provide more optimal choices for the multi-objective design of GMDH-type neural networks.
机译：本文将进化算法（EAs）用于多目标帕累托群方法数据处理（GMDH）型神经网络的优化设计，该神经网络已用于疲劳寿命建模和碳纤维增强的单向（UD）预测使用输入输出实验数据的塑料（CFRP）复合材料。用于这种建模的输入参数是一个周期内的应力比，循环应变能，纤维取向角，最大应力和破坏应力水平。这样，首先提出了具有新编码方案的EA，用于通用GMDH型神经网络的进化设计，其中，此类网络中的连接配置不限于相邻层。其次，将具有新的多样性保留机制的多目标EA用于此类GMDH型神经网络的Pareto优化。这项工作中考虑的GMDH型神经网络的重要冲突目标是训练误差（TE），预测误差（PE）和神经元数量（N）。这些目标函数的不同对被选择用于两个目标的优化过程。因此，在每种情况下都可获得此类模型的最优Pareto前沿，这些最优Pareto前沿展现了在对应的一对冲突目标之间的取舍，从而为UD的疲劳寿命提供了GMDH型神经网络模型的不同非主导性最优选择。 CFRP复合材料。此外，在三个目标的优化过程中考虑了所有三个目标，因此导致了GMDH类型模型的更多非主导选择，这些模型代表了TE，PE和N（网络复杂性）之间的权衡，同时。这些Pareto前沿的比较图还显示，三个目标的结果包括两个目标的结果，从而为GMDH型神经网络的多目标设计提供了更多最佳选择。

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  • 作者

    K Salmalian; N Nariman-Zadeh; H Gharababei; H Haftchenari; A Varvani-Farahani;

  • 作者单位

    Faculty of Engineering, Department of Mechanical Engineering, The University of Guilan, Rasht, Iran;

    Faculty of Engineering, Department of Mechanical Engineering, The University of Guilan, Rasht, Iran Faculty of Engineering, Intelligent-Based Experimental Mechanics Center of Excellence, School of Mechanical Engineering, University of Tehran, Tehran, Iran Department of Mechanical Engineering, The University of Guilan, PO Box 3756, Rasht, Iran;

    Faculty of Engineering, Department of Mechanical Engineering, The University of Guilan, Rasht, Iran;

    Faculty of Engineering, Department of Mechanical Engineering, The University of Guilan, Rasht, Iran;

    Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, Canada;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    fatigue life; carbon-fibre-reinforced plastics composites; genetic algorithms; group method of data handling; pareto;

    机译：疲劳寿命碳纤维增强塑料复合材料;遗传算法;分组数据处理方法;帕雷托;

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