Abstract
摘要: 经典模糊系统构建方法训练时通常仅考虑单一的场景,其伴随的一个重要缺陷是: 如当前场景重要信息缺失,则受训所得系统泛化能力较差.针对此问题, 以Mamdani-Larsen (ML)型模糊系统为对象,探讨了具有迁移学习能力的模糊系统, 即ML型迁移学习模糊系统. ML型迁移学习模糊系统不仅能充分利用当前场景的数据信息, 而且能有效地利用历史知识来进行学习,具有通过迁移历史场景知识来弥补当前场景信息 缺失的能力.具体地,基于经典的压缩集密度估计(Reduced set density estimator, RSDE) ML型模糊系统构建方法, 通过引入迁移学习机制提出了一种基于密度估计的ML型迁移模糊系统构建方法. 在模拟数据和真实数据上的实验研究亦验证了该迁移模糊系统在信息缺失场景下较之于 传统模糊系统建模方法的更好适应性. 关键词: 迁移学习 / 信息缺失 / 压缩集密度估计 / Mamdani-Larsen模糊系统
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