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- W2003296502 abstract "This paper presents a message-passing architecture simulating multilayer neural networks, adjusting its weights for each pair, consisting of an input vector and a desired output vector. First, the multilayer neural network is defined, and the difficulties arising from parallel implementation are clarified using Petri nets. Then the implementation of a neuron, split into the synapse and body, is proposed by arranging virtual processors in a cascaded torus topology. Mapping virtual processors onto node processors is done with the intention of minimizing external communication. Then, internal communication is reduced and implementation on a physical message-passing architecture is given. A time complexity analysis arises from the algorithm specification and some simplifying assumptions. Theoretical results are compared with experimental ones measured on a transputer based machine. Finally the algorithm based on the splitting operation is compared with a classical one." @default.
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- W2003296502 title "A parallel algorithm for gradient training of feedforward neural networks" @default.
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