基于模糊逻辑的增强集成深度学习技术 (EIFL-DL) 用于工业应用的推系统
Yasir Rafique1, Jue Wu1, Abdul Wahab Muzaffar2
1School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China.
PeerJ. Computer science
|December 9, 2024
概括
本研究介绍了一种基于模糊逻辑的增强集成深度学习 (EIFL-DL) 技术,用于改善工业应用的推系统 (RS). 通过结合模糊逻辑和深度学习来处理复杂的工业数据,EIFL-DL框架提高了建议的准确性和可解释性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 推系统 (RSs) 对于工业环境中的个性化至关重要.
- 传统的RS与快速发展的工业数据的复杂性和不确定性作斗争.
- 需要先进的技术来提高建议的准确性和解释性.
研究的目的:
- 提出一个增强的基于模糊逻辑的深度学习 (EIFL-DL) 框架.
- 解决传统RS在处理工业数据挑战方面的局限性.
- 提高建议在工业应用中的准确性和可解释性.
主要方法:
- EIFL-DL框架集成了用于处理不确定性的模糊逻辑和用于提取模式的深度学习.
- 数据预处理包括清理,规范化和转化为模糊集.
- 功能提取利用深度学习模型,如CNN和RNN.
- 推生成采用模糊逻辑规则和混合算法.
主要成果:
- 欧洲投资基金-DL框架有效地处理工业数据中的不确定性和模糊性.
- 深度学习组件提取复杂的模式,以增强功能表示.
- 综合方法克服了传统RS的局限性,产生了准确和可解释的建议.
结论:
- EIFL-DL框架为工业环境中的个性化建议提供了一个强大的解决方案.
- 结合模糊逻辑和深度学习,显著提高了RS性能.
- 这种方法为更复杂的工业推系统提供了基础.
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