Sparsely connected autoassociative fuzzy implicative memories and their application for the reconstruction of large gray-scale images

作者：Valle Marcos Eduardo^*

来源：Neurocomputing, 2010, 74(1-3): 343-353.

DOI：10.1016/j.neucom.2010.03.017

• Fuzzy associative memory
• Large gray scale images
• Sparse encoding
• Storage capacity
• Noise tolerance

Autoassociative fuzzy implicative memories (AFIMs) are models that exhibit optimal absolute storage capacity and an excellent tolerance with respect to incomplete or eroded patterns As a consequence they can be effectively used for the reconstruction of gray-scale images In practice however applications of AFIMs are confined to Images of small size due to computational limitations In order to circumvent this computational overhead and motivated by the sparsity of biological neural networks this paper introduces the class of sparsely connected AFIMs (SCAFIMs) Such as the original AFIMs SCAFIMs exhibit optimal absolute storage capacity and tolerance with respect to Incomplete or eroded patterns By means of computational experiments we investiga

• 出版日期2010-12