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基于PSO-BP优化神经网络和网络技术的地下发电厂周围岩石参数的云逆转分析
Long Qu1, Hong-Qiang Xie2, Jian-Liang Pei1
1State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu, 610065, China.
这项研究优化了BP神经网络,使用粒子群优化 (PSO) 为地下工程安全. 开发的云程序为周围岩石参数提供了准确,智能反分析.
科学领域:
- 地质技术工程 地质技术工程
- 人工智能的人工智能
- 云计算 云计算 云计算 云计算
背景情况:
- 传统的BP神经网络遭受局部极端和缓慢的融合.
- 地下工程安全监测需要准确有效的分析方法.
研究的目的:
- 为了提高BP神经网络的性能,用于地下工程安全.
- 开发一个智能云端反分析程序,使用PSO-BP.
- 在现实世界的工程应用中验证程序的准确性.
主要方法:
- 使用粒子优化 (PSO) 优化了BP神经网络的初始权重和值.
- 集成云计算,网络技术,云数据库和数值模拟.
- 开发了一个基于PSO-BP算法的智能反分析云程序.
主要成果:
- 该PSO-BP算法改善了BP神经网络的融合,并避免了局部极端.
- 智能反分析云程序实现了方便,快速和智能化的分析.
- 该程序应用于Shuangjiangkou水电站,准确地对周围岩石参数进行了反向分析.
- 模拟的位移与测量的位移密切匹配,后部误差比为0.045和小误差概率为0.999.
结论:
- 智能反分析云程序展示了高准确性和可靠性.
- 开发的系统适用于实际的地下工程安全监测.
- 集成PSO-BP算法显著增强神经网络能力,解决复杂的工程问题.
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