Adaptive learning of fuzzy BSB and GBSB neural models
Cybernetics and Systems Analysis
Qualitative Analysis of General Discrete-Time Recurrent Neural Networks with Impulses
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
A new algorithm for implementing BSB-based associative memories
ICC'08 Proceedings of the 12th WSEAS international conference on Circuits
Permitted and forbidden sets in discrete-time linear threshold recurrent neural networks
IEEE Transactions 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'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Brief Associative memory design using overlapping decompositions
Automatica (Journal of IFAC)
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In this paper, some new qualitative properties of discrete-time neural networks based on the “brain-state-in-a-box” model are presented. These properties concern both the characterization of equilibrium points and the global dynamical behavior. Next, the analysis results are used as guidelines in developing an efficient synthesis procedure for networks that function as associative memories. A constrained design algorithm is presented that gives completely stable dynamical neural networks sharing some interesting features. It is guaranteed the absence of nonbinary stable equilibria, that is stable states with nonsaturated components. It is guaranteed that in close proximity (Hamming distance one) of the stored patterns there is no other binary equilibrium point. Moreover, the presented method allows one to optimize a design parameter that controls the size of the attraction basins of the stored vectors and the accuracy needed in a digital realization of the network