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基于改进的FCM-BiLSTM进行概念学习,用于模糊数据分类和融合

Jiaojiao Niu¹, Jiankun Zuo², Wenyan Tie²

¹School of Computer Science, Yangtze University, Jingzhou, 434023, China. njjiao_92@163.com.

Scientific reports

|August 12, 2025

概括

本研究引入了一种新的模糊概念-认知学习模型 (FCLSCL),通过解决不准确的标签和对象依赖来改善概念学习. 新模型增强了复杂数据集中的知识发现.

科学领域:

人工智能的人工智能
机器学习机器学习
认知科学认知科学

背景情况:

概念认知学习 (CCL) 模拟了人类的认知,以发现知识.
现有的CCL方法因不准确的标签和对象依赖而扎,限制了适应性.

研究的目的:

提出一个新的模糊概念-认知学习模型 (FCLSCL),解决当前CCL方法的局限性.
通过整合模糊集群和深度学习来增强概念学习,以提高准确性和适应性.

主要方法:

开发了FCLSCL,集成了改进的模糊C介质 (FCM) 和双向长短期记忆网络 (BiLSTM).
引入加权模糊概念,通过考虑会员级别和伪概念来管理数据不确定性.
利用连接的模糊概念作为BILSTM的输入,以实现双向概念学习.

主要成果:

拟议的FCLSCL模型与流行的机器学习模型,现有的CCL方法和LSTM相比,显示出更高的有效性.
该模型成功地解决了与概念学习中的不准确标签和对象依赖性相关的挑战.
实验结果验证了模型处理各种数据集和复杂的关系模式的能力.

结论:

通过有效处理数据不确定性和复杂关系,FCLSCL在概念学习方面取得了重大进展.
模糊集群和BiLSTM的整合为知识发现提供了一个强大的框架.
FCLSCL模型对需要准确且可适应的概念分类的应用具有前景.

关键词:

这就是为什么BiLSTM.概念分类的概念分类.概念 - 认知学习学习模糊的聚类模糊的聚类.

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