模糊的适应性基于知识的推断神经网络:设计和分析.
IEEE transactions on cybernetics
|February 28, 2024
概括
一个新的模糊适应性基于知识的推断神经网络 (FAKINN) 克服了复杂数据模糊规则提取的局限性. 这种新的方法增强了概括能力,特别是对于高维数据集.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算智能是一种计算智能.
背景情况:
- 传统的基于模糊集群的神经网络 (FCBNNs) 难以从复杂的数据结构中提取模糊规则,限制它们表示数据异质性和同质性的能力.
- 随着数据维度的增加,FCBNN的规则生成能力下降,阻碍了准确的推断和概括.
- 现有的方法在有效地捕捉类间异质性和类内同质性方面面临挑战,这会影响模糊的基于规则的系统的性能.
研究的目的:
- 提出一种新的模糊适应性基于知识的推断神经网络 (FAKINN),旨在克服传统FCBNNs的局限性.
- 通过改进模糊规则提取来提高模糊神经网络的概括能力,特别是对于复杂和高维数据.
- 引入一个自适应知识生成器 (AKG),有效地提炼特征信息,并将其总结成强大的模糊规则.
主要方法:
- 开发一个自适应知识生成器 (AKG),包括一个观察范式 (OP) 和一个集群策略 (CS).
- OP提炼了特征信息 (CI),以突出数据的同质性和异质性.
- 实施加权条件驱动模糊集群方法 (WCFCM) 来总结CI并构建模糊规则,并使用反机制来控制CI维度以处理高维数据.
主要成果:
- 与27种基准方法相比,FAKINN在各种数据集中表现出卓越的性能.
- 拟议的AKG,包括OP和WCFCM,有效地解决了传统FCBNN在模糊规则生成中的局限性.
- 对现实世界问题的实验验证证证了FAKINN的有效性和改进的泛化能力,特别是在高维数据方面.
结论:
- FAKINN在模糊神经网络设计方面取得了重大进展,特别是在复杂和高维数据集方面.
- 新的AKG和WCFCM为模糊神经网络的结构设计提供了强大的方法,增强了规则提取和概括.
- 拟议的方法性能优于现有方法,突出了它对各种机器学习应用的潜力,这些应用需要有效的模糊规则推断.
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