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- W2002159781 abstract "This paper studies the simple dynamic binary neural network characterized by the signum activation function, ternary weighting parameters and integer threshold parameters. The network can be regarded as a digital version of the recurrent neural network and can output a variety of binary periodic orbits. The network dynamics can be simplified into a return map, from a set of lattice points, to itself. In order to store a desired periodic orbit, we present two learning algorithms based on the correlation learning and the genetic algorithm. The algorithms are applied to three examples: a periodic orbit corresponding to the switching signal of the dc-ac inverter and artificial periodic orbit. Using the return map, we have investigated the storage of the periodic orbits and stability of the stored periodic orbits." @default.
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- W2002159781 date "2013-01-01" @default.
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- W2002159781 title "Learning of Simple Dynamic Binary Neural Networks" @default.
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- W2002159781 doi "https://doi.org/10.1587/transfun.e96.a.1775" @default.
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