  Construction and training principles have been proposed and tested for an
artificial neural network based on metal-oxide thin-film nanostructures
possessing bipolar resistive switching (memristive) effect. Experimental
electronic circuit of neural network is implemented as a double-layer
perceptron with a weight matrix composed of 32 memristive devices. The network
training algorithm takes into account technological variations of the
parameters of memristive nanostructures. Despite the limited size of weight
matrix the developed neural network model is well scalable and capable of
solving nonlinear classification problems.
