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首页> 外文期刊>Proceedings of the institution of mechanical engineers >Multi-objective optimum design of ANFIS for modelling and prediction of deformation of thin plates subjected to hydrodynamic impact loading
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Multi-objective optimum design of ANFIS for modelling and prediction of deformation of thin plates subjected to hydrodynamic impact loading

机译：ANFIS的多目标优化设计，用于流体冲击载荷作用下的薄板变形建模和预测

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摘要

Drop hammer impact experiments have been carried out to assess the dynamic plastic response of fully clamped circular and rectangular plates made of aluminum and steel subjected to hydrodynamic impact loading at various energy levels. Also, the effective parameters in forming process are proposed in non-dimensional forms for modeling and prediction of the central deflection of plates using adaptive neuro-fuzzy inference system in conjunction with genetic algorithm and singular value decomposition method. Genetic algorithm is used for optimal scheme of Gaussian membership function's variables and multi-objective Pareto optimal design of adaptive neuro-fuzzy inference system model. Also, the singular value decomposition method is applied to compute the linear parameters of the adaptive neuro-fuzzy inference system method. The important conflicting objectives of developed adaptive neuro-fuzzy inference system, namely, training error and prediction error, are obtained by dividing date sets into two parts. Hence, various optimal choices of adaptive neuro-fuzzy inference system model are provided which are non-dominated states from each other. Moreover, optimal Pareto front of such model leads to trade-off between the conflicting pair of considered objectives for two series of experiments. The results of this work indicate that multi-objective Pareto optimal design of adaptive neuro-fuzzy inference system predicts central deflection of plates with a good accuracy. In addition, the comparison between the adaptive neuro-fuzzy inference system model and exiting one demonstrates superior performance of the present approach in simulating central deflection of plates.
机译：已经进行了落锤冲击实验，以评估在各种能级下承受流体冲击载荷的铝和钢制成的完全夹紧的圆形和矩形板的动态塑性响应。此外，采用自适应神经模糊推理系统结合遗传算法和奇异值分解方法，以无量纲形式提出了成形过程中的有效参数，以对板的中心变形进行建模和预测。遗传算法用于高斯隶属函数变量的最优方案和自适应神经模糊推理系统模型的多目标帕累托最优设计。同样，将奇异值分解方法用于计算自适应神经模糊推理系统方法的线性参数。通过将日期集分为两部分，可以得出已开发的自适应神经模糊推理系统的重要冲突目标，即训练误差和预测误差。因此，提供了自适应神经模糊推理系统模型的各种最优选择，它们彼此是非支配状态。此外，这种模型的最优帕累托前沿导致两个系列实验的考虑目标之间的冲突。这项工作的结果表明，自适应神经模糊推理系统的多目标帕累托最优设计可以很好地预测板块的中心偏转。另外，在自适应神经模糊推理系统模型与现有模型之间的比较证明了本方法在模拟板的中心偏转方面的优越性能。

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