Overlapping vs partitioning in block-iteration methods: application in large-scale system theory
Automatica (Journal of IFAC)
Analysis and synthesis of a class of discrete-time neural networks described on hypercubes
IEEE Transactions on Neural Networks
Synthesis of Brain-State-in-a-Box (BSB) based associative memories
IEEE Transactions on Neural Networks
A synthesis procedure for brain-state-in-a-box neural networks
IEEE Transactions on Neural Networks
Large-scale pattern storage and retrieval using generalized brain-state-in-a-box neural networks
IEEE Transactions on Neural Networks
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This paper discusses the use of decomposition techniques in the design of associative memories via artificial neural networks. In particular, a disjoint decomposition which allows an independent design of lower-dimensional subnetworks and an overlapping decomposition which allows subnetworks to share common parts, are analyzed. It is shown by a simple example that overlapping decompositions may help in certain cases where design by disjoint decompositions fails. With this motivation, an algorithm is provided to synthesize neural networks using the concept of overlapping decompositions. Applications of the proposed design procedure to a benchmark example from the literature and to a pattern recognition problem indicate that it may improve the effectiveness of the existing methods.