Synergetic computers and cognition: a top-down approach to neural nets
Synergetic computers and cognition: a top-down approach to neural nets
Entropy Based Associative Memory
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
IEEE Transactions on Computers
Representation of Associated Data by Matrix Operators
IEEE Transactions on Computers
Entropy based associative model
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Auto-associative memory with two-stage dynamics of nonmonotonic neurons
IEEE Transactions on Neural Networks
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In this paper, a generalised entropy based associative memory model will be proposed and applied to memory retrievals with analogue embedded vectors instead of the binary ones in order to compare with the conventional autoassociative model with a quadratic Lyapunov functionals. In the present approach, the updating dynamics will be constructed on the basis of the entropy minimization strategy which may be reduced asymptotically to the autocorrelation dynamics as a special case. From numerical results, it will be found that the presently proposed novel approach realizes the larger memory capacity even for the analogue memory retrievals in comparison with the autocorrelation model based on dynamics such as associatron according to the higher-order correlation involved in the proposed dynamics.