针对 Kosko提出的最大最小模糊联想记忆网络存在的问题，通过对这种网络连接权学习规则的改进，给出了另一种权重学习规则，即把 Kosko的前馈模糊联想记忆模型发展成为模糊双向联想记忆模型，并由此给出了模糊快速增强学习算法，该算法能存储任意给定的多值训练模式对集.其中对于存储二值模式对集，由于其连接权值取值 0或 1,因而该算法易于硬件电路和光学实现.实验结果表明，模糊快速增强学习算法是行之有效的.

This paper gives a new learning rule about the formation of weights for two-layer max-min feedforward fuzzy associative memory (FAM) network proposed by Kosko . Based on the new rule,The feedforward FAM model is developed into a fuzzy bidirectional associative memory (BAM) model,and a fuzzy quick augmentation algorithm is also proposed,Its stability and tolerance for the BAM model are also analyzed. From the analysis, an interesting result which can store an arbitrary given multi-value patterns is obtained. When used to store binary values, The weights for BAM model take binary too, 0 or 1.So it is suitable for the VLSI and optical implementation. In order to make a comparision, binary based sample patterns have adoped. A larger number of simulation results show the advantages of a less number of weighted value,or the simple implementation, by comparing with the existing learning algorithm,such as binary based Hoperfield dummy augmentation and MBDS augmentation algorithms. On the other hand, the fuzzy quick augmentation algirithm has the merit of the simpler computation and faster convergence.