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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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使用基于优化的算法和级联前进神经网络,提高了回收聚合混凝土混凝土压力强度的预测准确性

Xiaoguang Zhou1, Jian Zhou2, Joy P Ohl3

  • 1State Key Laboratory of Precision Blasting, Jianghan University, Wuhan, 430056, China; Hubei Key Laboratory of Blasting Engineering, Jianghan University, Wuhan, 430056, China.

Journal of environmental management
|October 30, 2024
PubMed
概括

本研究介绍了一种基于精英单基因优化算法的级联前进神经网络 (ESGA-CFNN) 模型,以准确预测回收聚合混凝土 (RAC) 的压力强度. 该ESGA-CFNN模型展示了卓越的性能,增强了可持续的建筑实践.

关键词:
级联前进神经网络级联神经网络.压力强度预测的预测精英单个遗传优化算法精英机器学习是机器学习.回收聚合混凝土 (RAC) 的使用可持续的建筑材料 可持续的建筑材料

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