基于均衡优化器的LSTM应用在水库识别中的应用
Fan Yang1, Kewen Xia1, Shurui Fan1
1Hebei University of Technology, College of Electronic Information Engineering, Tianjin 300401, China.
Computational intelligence and neuroscience
|October 16, 2024
概括
本研究介绍了一种新的TAFEO算法,用于优化长期短期记忆 (LSTM) 网络,以改善井记录中的水库识别. 改进后的LSTM模型实现了高精度,性能优于现有方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 地质科学是地球科学.
背景情况:
- 在井记录中,水库识别至关重要,但也是具有挑战性的.
- 长期短期记忆 (LSTM) 网络表现有前途,但存在局限性.
- 优化LSTM参数是提高分类准确性的关键.
研究的目的:
- 为了提高基于LSTM的水库识别在井记录中的准确性.
- 为LSTM参数优化引入一个改进的等分优化算法 (TAFEO).
- 评估TAFEO优化的LSTM模型的有效性.
主要方法:
- 开发了基于帐混乱映射的均衡优化算法 (TAFEO).
- 应用TAFEO来优化LSTM神经元和参数用于储库识别.
- 使用基准函数和威尔科克森等级和和测试验证实TAFEO.
- 使用接收器操作特征 (ROC) 曲线和UCI数据集评估了优化的LSTM模型.
主要成果:
- 与其他优化算法相比,TAFEO表现出卓越的准确性和融合速度.
- 在TAFEO优化的LSTM模型中,在UCI数据集上,ROC曲线下的最大面积 (AUC) 为99.43%.
- 在实际的井记录应用中,TAFEO优化的LSTM模型达到95.01%的识别精度.
结论:
- 塔菲奥算法有效优化LSTM,用于增强水库识别.
- 拟议的方法显著提高了井记录应用中的准确性和稳定性.
- 与现有方法相比,这种方法为水库识别提供了更有效的解决方案.
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