煤矿安全预警系统：利用遗传算法优化改进BP神经网络
Coal Mine Safety Alert System: Refining BP Neural Network with Genetic Algorithm Optimization
AI总结：
提出融合遗传算法与三层前馈BP神经网络的GA-BP煤矿安全预警模型，并在山东煤矿数据上验证其风险识别能力优于传统BP网络。
摘要：
针对煤矿内部持续存在的安全挑战，本研究提出了一种将三层前馈反向传播人工神经网络与遗传算法（GA-BP）相结合的新方法，用于建立安全预警系统。研究以中国山东的一座煤矿为对象，利用相关数据进行训练和分析，以评估模型的有效性。结果表明，GA-BP模型优于传统BP神经网络，能够增强及时识别潜在安全风险的能力。这一进展使煤矿管理层能够实施及时干预，确保矿工安全。研究结果为类似情境下的工程应用提供了宝贵见解。
英文摘要：
In response to the persistent safety challenges within coal mines, this study proposes a novel approach integrating a three-layer feedforward backpropagation artificial neural network with a genetic algorithm (GA-BP) for establishing a safety early warning system. Focused on a coal mine in Shandong, China, the model's effectiveness is evaluated using relevant data for training and analysis. Results indicate the superiority of the GA-BP model over traditional BP neural networks, offering enhanced capability for identifying potential safety risks promptly. This advancement enables coal mine management to implement timely interventions, ensuring the safety of miners. The findings present valuable insights for engineering applications in similar contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
