On the brain-state-in-a-convex-domain neural models
Neural Networks
The BSB neural network in the convex body spanned by the prototype patterns for associative memory
Applied Mathematics and Computation
Analysis of the BSB Model Dynamics Using Control Theory
Neural Processing Letters
A reference model approach to stability analysis of neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neural associative memory storing gray-coded gray-scale images
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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In this paper, a design procedure is presented for synthesizing associative memories based on the Brain-State-in-a-Box neural network model. The theoretical analysis herein guarantees that the desired memory patterns are stored as asymptotically stable equilibrium points with very few spurious states. In order to avoid extensive computation, learning and forgetting are utilized by adding patterns to be stored as asymptotically stable equilibrium points to an existing set of stored patterns and deleting specified patterns from a given set of stored patterns without affecting the rest in a given network. Furthermore, the number of the memorized patterns in a designed Brain-State-in-a-Box neural network model can be made much more than that of neurons. Simulation results demonstrate the validity and characteristics of the proposed approach.