The :20Brain-state-in-a-box" Neural model is a gradient descent algorithm
Journal of Mathematical Psychology
On the stability, storage capacity, and design of continuous nonlinear neural networks
IEEE Transactions on Systems, Man and Cybernetics
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Stability and optimization analyses of the generalized brain-state-in-a-box neural network model
Journal of Mathematical Psychology
The BSB model: a simple nonlinear autoassociative neural network
Associative neural memories
Dynamics and stability analysis of the Brain-State-in-a-Box (BSB) neural models
Associative neural memories
SIAM Review
Artificial Social Systems: 4th European Workshop on Modelling Autonomous Agents in a Multi-Agent World MAAMAW '92, S. Martino Al Cimino, Italy, July 29-31 1992
A new neural network for solving linear programming problems and its application
IEEE Transactions on Neural Networks
Neural network for solving linear programming problems with bounded variables
IEEE Transactions on Neural Networks
A synthesis procedure for brain-state-in-a-box neural networks
IEEE Transactions on Neural Networks
An Extended Projection Neural Network for Constrained Optimization
Neural Computation
A new neural network for solving nonlinear projection equations
Neural Networks
Large-scale pattern storage and retrieval using generalized brain-state-in-a-box neural networks
IEEE Transactions on Neural Networks
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
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In this article we present techniques for designing associative memories to be implemented by a class of synchronous discrete-time neural networks based on a generalization of the brain-state-in-a-box neural model. First, we address the local qualitative properties and global qualitative aspects of the class of neural networks considered. Our approach to the stability analysis of the equilibrium points of the network gives insight into the extent of the domain of attraction for the patterns to be stored as asymptotically stable equilibrium points and is useful in the analysis of the retrieval performance of the network and also for design purposes. By making use of the analysis results as constraints, the design for associative memory is performed by solving a constraint optimization problem whereby each of the stored patterns is guaranteed a substantial domain of attraction. The performance of the designed network is illustrated by means of three specific examples.