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通过增强的BP神经网络和多目标NSGA-II进行耐老化改性的性能预测和过程优化

Shanwei Li¹, Shaojie Gao¹, Jiangtao Fan²

¹The Key Laboratory of Intelligent Construction and Maintenance of CAAC, Chang'an University, Xi'an 710064, China.

Materials (Basel, Switzerland)

|December 11, 2025

概括

这项研究引入了混合生物灵感模型 (CPO-BP和DBO-BP) 来预测抗衰老青的性能. 该CPO-BP模型取得了卓越的准确性,确定混合温度是平衡路阻力和可塑性的关键.

科学领域:

材料科学材料科学材料科学
土木工程土木工程是指土木工程.
计算智能是一种计算智能.

背景情况:

由于复杂的参数-属性关系,预测抗衰老青的性能具有挑战性.
生物启发的算法为提高材料科学的预测准确性提供了潜力.

研究的目的:

开发和评估混合模型,将生物灵感算法与神经网络相结合,用于对抗衰老青的多目标预测.
确定影响青性能的关键准备参数,并优化它们以提高稳定性和耐力.

主要方法:

将猪优化器 (CPO) 和土虫优化器 (DBO) 与反向传播 (BP) 神经网络集成,以创建CPO-BP和DBO-BP模型.
应用沙普利增量解释 (SHAP) 来识别影响因素和特征相互作用.
使用非主导排序遗传算法II (NSGA-II) 的优化和通过原子力显微镜 (AFM) 的微结构性表征.

主要成果:

与标准BP和DBO-BP模型相比,CPO-BP模型显示出更高的预测准确性.
混合温度被确定为最有影响的因素,影响了腐蚀性,柔性和耐老化性.
观察到混合温度和剪切时间之间的协同效应,以及含量和温度之间的合效应.

结论:

混合生物启发的神经网络模型为预测抗衰老青性能提供了强大的框架.
数据驱动的预测和多目标优化对于高性能材料的合理设计至关重要.
了解参数相互作用是优化青性能以满足各种性能要求的关键.

关键词:

这是NSGA-II算法.在SHAP分析中,我们分析了SHAP.尖顶的猪优化优化甲虫优化甲虫优化青青青青

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