On-chip learning with analogue VLSI neural networks.
Results from simulations of weight perturbation as an on-chip learning scheme for analogue VLSI neural networks are presented. The limitations of analogue hardware are modelled as realistically as possible. Thus synaptic weight precision is defined according to the smallest change in the weight setting voltage which gives a measurable change at the output of the corresponding neuron. Tests are carried out on a hard classification problem constructed from mobile robot navigation data. The simu...Expand abstract
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