Alpha---Beta bidirectional associative memories: theory and applications
Neural Processing Letters
Robustness of radial basis functions
Neurocomputing
Analysis on Bidirectional Associative Memories with Multiplicative Weight Noise
Neural Information Processing
A Bidirectional Hetero-Associative Memory for True-Color Patterns
Neural Processing Letters
IEEE Transactions on Neural Networks
Complexity of alpha-beta bidirectional associative memories
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
A new model of BAM: alpha-beta bidirectional associative memories
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
A new learning strategy of general BAMs
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Bidirectional associative memories: Different approaches
ACM Computing Surveys (CSUR)
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In this paper, the basic bidirectional associative memory (BAM) is extended by choosing weights in the correlation matrix, for a given set of training pairs, which result in a maximum noise tolerance set for BAM. We prove that for a given set of training pairs, the maximum noise tolerance set is the largest, in the sense that this optimized BAM will recall the correct training pair if any input pattern is within the maximum noise tolerance set and at least one pattern outside the maximum noise tolerance set by one Hamming distance will not converge to the correct training pair. This maximum tolerance set is the union of the maximum basins of attraction. A standard genetic algorithm (GA) is used to calculate the weights to maximize the objective function which generates a maximum tolerance set for BAM. Computer simulations are presented to illustrate the error correction and fault tolerance properties of the optimized BAM.