Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
Linear optimization and extensions: theory and algorithms
Linear optimization and extensions: theory and algorithms
Analysis and synthesis of associative memories based on brain-state-in-a-box neural networks
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new synthesis approach for feedback neural networks based on the perceptron training algorithm
IEEE Transactions on Neural Networks
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ICEC'10 Proceedings of the 9th international conference on Entertainment computing
Synchronization for a class of uncertain chaotic cellular neural networks with time-varying delay
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
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Associative memories are brain-style devices designed to store a set of patterns as stable equilibria such that the stored patterns can be reliably retrieved with the initial probes containing sufficient information about the patterns. This paper presents a new design procedure for synthesizing associative memories based on continuous-time cellular neural networks with time delays characterized by input and output matrices obtained using two-dimensional space-invariant cloning templates. The design procedure enables hetero-associative or auto-associative memories to be synthesized by solving a set of linear inequalities with few design parameters and retrieval probes feeding from external inputs instead of initial states. The designed associative memories are robust in terms of design parameter selection. In addition, the hosting cellular neural networks are guaranteed to be globally exponentially stable. Simulation and experimental results of illustrative examples and Monte Carlo tests demonstrate the applicability and superiority of the methodology.