Information processing using a model of associative memory
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
IEEE Transactions on Computers
A Quantitative Comparison of the Performance of Three Discrete Distributed Associative Memory Models
IEEE Transactions on Computers
An Adaptive Associative Memory Principle
IEEE Transactions on Computers
Convergence in Iteratively Formed Correlation Matrix Memories
IEEE Transactions on Computers
A Generalised Entropy Based Associative Model
Neural Information Processing
Pattern recall analysis of the Hopfield neural network with a genetic algorithm
Computers & Mathematics with Applications
Entropy based associative model
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Engineering Applications of Artificial Intelligence
International Journal of Hybrid Intelligent Systems
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It is shown that associated pairs of vectoral items (Q(r), X(r)) can be recorded by transforming them into a matrix operator M so that a particular stored vector X(r) can be reproduced by multiplying an associated cue vector Q(r) by M. If the number of pairs does not exceed the dimension of the cue and all cue vectors are linearly independent, then the recollections are perfect replicas of the recorded items and there will be no crosstalk from the other recorded items. If these conditions are not valid, the recollections are still linear least square approximations of the X(r). The relationship of these mappings to linear estimators is discussed. These transforms can be readily implemented by linear analog systems.