The BSB neural network in the convex body spanned by the prototype patterns for associative memory
Applied Mathematics and Computation
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
Analysis and synthesis of associative memories based on brain-state-in-a-box neural networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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We study a generalization of the brain-state-in-a-box (BSB) model for a class of nonlinear discrete dynamical systems where we allow the states of the system to lie in an arbitrary convex body. The states of the classical BSB model are restricted to lie in a hypercube. Characterizations of equilibrium points of the system are given using the support function of a convex body. Also, sufficient conditions for a point to be a stable equilibrium point are investigated. Finally, we study the system in polytopes. The results in this special case are more precise and have simpler forms than the corresponding results for general convex bodies. The general results give one approach of allowing pixels in image reconstruction to assume more than two values