Properties of Fuzzy Morphological Bidirectional Associative Memories
ZENG Shui-Ling1,2, XU Wei-Hong2,3, YANG Jing-Yu2
1.College of Information Science and Engineering,Jishou University,Jishou 416000 2.College of Computer Science and Technology,Nanjing University of Science and Technology,Nanjing 210094 3.College of Computer and Communications Engineering,Changsha University of Science and Technology,Changsha 410077
Abstract 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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2012, Vol. 25 
