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中文摘要：
为模糊形态学双向联想记忆网络（FMBAM）提出一个学习算法.在理论上证明只要存在使给定的模式对集合成为FMBAM的平衡态集合,则该学习算法总能计算出相应的最大连接权矩阵对.该最大连接权矩阵对能使FMBAM对任意输入在一步内就进入平衡态,并且神经网络全局收敛到平衡态.FMBAM的每个平衡态都是Lya-punov稳定的.当训练模式存在摄动时,利用该学习算法训练的FMBAM,对训练模式摄动拥有好的鲁棒性.
英文摘要：
A learning algorithm is proposed for a class of fuzzy morphological bidirectional associative memories（FMBAM）.It is proved theoretically that,for any given set of pattern pairs,if existing pairs of connection weight matrices which make the set become a set of the equilibrium states of FMBAM,the proposed learning algorithm can give the maximum of all such pairs of weight matrices.And the learning algorithm ensure that the FMBAM with this maximal pair of connection weight matrices can be convergent to an equilibrium state in one iterative process for any input.Any equilibrium state of FMBAM is Lyapunov stable.FMBAM can converge to equilbrium state for its any input vector.The robustness of FMBAM is good when the learning algorithm is used to train FMBAM and training pattern pairs have perturbations.
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