Precise characterization of memristive systems by cooperative artificial neural networks

Nowadays, there are many emerging electronic structures for which their nonlinear models for CAD are necessary, especially for the ones from the area of nanoelectronics. However, for such structures, sufficiently accurate analytic models are mostly unavailable. This is partially caused by the fact that the physical principles of the element operation are sometimes not fully clear (especially for quantum devices), and also by bizarre characteristics of some elements (typically with irregularities and a hysteresis in parts of characteristics). In such cases, models based on artificial neural networks are necessary and useful for these elements. Majority of the elements can be characterized with a single artificial neural network. However, for certain kinds of elements, a cooperation of more artificial networks is necessary. This case is described in the paper, where the Pt - TiO_2-x - Pt memristor characteristic with an extraordinary (but typical) hysteresis is approximated by a set of cooperative artificial neural networks, as a single network is unable to characterize this unconventional element. Moreover, a semiautomatic selection of an optimal structure of the networks (both numbers of hidden layers and the numbers of the elements in the layers) is suggested in the paper.