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适应性混合ANFIS-PSO和ANFIS-GA方法用于职业风险预测

Mourad Achouri¹, Youcef Zennir², Cherif Tolba³

¹LRPCSI Laboratory Skikda, University 20 August 1955 Skikda, Algeria.

International journal of occupational safety and ergonomics : JOSE

|January 20, 2025

概括

这项研究优化了适应性神经模糊推理系统 (ANFIS) 用粒子群优化 (PSO) 来计算职业风险. 与其他机器学习方法相比,ANFIS-PSO模型显示出更高的预测准确度.

科学领域:

职业健康和安全问题职业健康和安全问题
计算智能是一种计算智能.
风险管理风险管理

背景情况:

适应性神经模糊推理系统 (ANFIS) 对于工程预测是有效的,但在风险评估中未得到充分利用.
准确的职业风险评估对于工作场所的安全和合规性至关重要.
优化技术可以提高风险分析中的预测模型的性能.

研究的目的:

优化适应性神经模糊推理系统 (ANFIS) 用于计算职业风险.
为了评估ANFIS的性能,该系统使用粒子集群优化 (PSO) 和遗传算法 (GA) 进行了优化.
将拟议的ANFIS模型与已建立的机器学习方法进行职业风险预测的比较.

主要方法:

适应性神经模糊推理系统 (ANFIS) 被用作核心预测模型.
使用粒子群优化 (PSO) 和遗传算法 (GA) 来优化ANFIS参数.
使用统计指数来评估性能,例如平均绝对误差 (MAE) 和根平均平方误差 (RMSE).
对比分析包括人工神经网络 (ANN),物流回归 (LR) 和支持矢量机器 (SVM).

主要成果:

使用粒子群优化 (ANFIS-PSO) 优化的ANFIS表现出卓越的预测性能.
与其他模型相比,ANFIS-PSO在训练和测试阶段都取得了更好的准确性.
拟议的ANFIS-PSO模型在与ANN,LR和SVM相比证明具有竞争力.

结论:

ANFIS-PSO模型是一个非常准确和适合用于职业风险预测的工具.
通过PSO优化ANFIS,提高了ANFIS应对复杂风险评估任务的能力.
该研究强调了先进的计算智能技术在提高职业安全方面的潜力.

关键词:

适应性神经模糊推理系统遗传算法是一种遗传算法.职业风险 - 职业风险是什么粒子群集优化粒子群集优化

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