High order correlation model for associative memory
AIP Conference Proceedings 151 on Neural Networks for Computing
The capacity of the Hopfield associative memory
IEEE Transactions on Information Theory
Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
Dynamic heteroassociative neural memories
Neural Networks
Better learning for bidirectional associative memory
Neural Networks
A generalized convergence theorem for neural networks
IEEE Transactions on Information Theory - Part 1
Two coding strategies for bidirectional associative memory
IEEE Transactions on Neural Networks
Guaranteed recall of all training pairs for bidirectional associative memory
IEEE Transactions on Neural Networks
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A novel hetero-associative neural network model is proposed where the associative recall of pattern is achieved in a single pass through the system. Instead of forming the memory matrix by an outer product formulation, inner product cross-correlation of input data with each set of the library vector was performed. The limitation regarding the constraint imposed on the choice or selection of patterns that can be stored is avoided by such a formulation. The reliability of the proposed model is much improved in comparison to the heteroassociative memory models which uses outer product correlation formulation to construct the memory matrix.