Comparative Analysis of Learning Methods of Cellular-Neural Associative Memory
PaCT '999 Proceedings of the 5th International Conference on Parallel Computing Technologies
A new algorithm for implementing BSB-based associative memories
ICC'08 Proceedings of the 12th WSEAS international conference on Circuits
Multiobjective algebraic synthesis of neural control systems by implicit model following
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An SVM based method for associative memories
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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In this paper, a new synthesis approach is developed for associative memories based on the perceptron training algorithm. The design (synthesis) problem of feedback neural networks for associative memories is formulated as a set of linear inequalities such that the use of perceptron training is evident. The perceptron training in the synthesis algorithms is guaranteed to converge for the design of neural networks without any constraints on the connection matrix. For neural networks with constraints on the diagonal elements of the connection matrix, results concerning the properties of such networks and concerning the existence of such a network design are established. For neural networks with sparsity and/or symmetry constraints on the connection matrix, design algorithms are presented. Applications of the present synthesis approach to the design of associative memories realized by means of other feedback neural network models are studied. To demonstrate the applicability of the present results and to compare the present synthesis approach with existing design methods, specific examples are considered