针对当前的各种纸病辨识方法只能对于一种或有限几种纸病有效辨识，且不能准确辨识难点纸病的问题，在全面分析纸病特征、研究和归纳各类纸病辨识方法的基础上，本文提出使用模糊融合器对纸病图像的多种特征值进行特征层融合，把多个纸病辨识方法结合在一起，以达到纸病高效、全面辨识的目的.由于径向基神经网络结构与模糊推理结构的等价性，使得径向基神经网络实现的多种纸病特征的信息融合系统具有结构简单和快速性的特点.实验表明：本文方法可以准确识别包括难点纸病在内的各种主要纸病.

Current paper defects identification methods has two radical problems. First, every current method can only identify one or few defects. Second, current methods can hardly detect the complex paper defects accurately. In view of these problems, based on comprehensive analysis of paper defect features, research and summary of all kinds of paper defect identification methods, Fuzzy fusion device is used to conduct feature layer fusion with some paper defect characteristic values, and combine multiple paper defect identification methods, aiming to achieve more efficient and comprehensive paper defect identification. According to the construction equivalence between RBF Neural Network and fuzzy reasoning, the paper defect features information fusion system on basis of RBFNN has the advantage of simple structure and rapidity. Experiments have shown that the method presented is practicable to identify the primary paper defects accurately, including complex paper defects.