We have successfully modelled the nanostructured memristor device characteristics using Artificial Neural Network.

In the present ANN architecture, the hidden units consist of nonlinear sigmoid activation functions, and training algorithm is based on a Levenberg–Marquardt Backpropogation method.

The performance of ANN architecture has been measured in terms of Mean Squared Error (MSE) and Pearson correlation coefficient (r).

Our results suggest that, ANN requires lower number of hidden neurons, lower test as well as validation data, hence present architecture can be used for hardware realization.

The hardware realization of the reported architecture has good number of applications such as neuromorphic applications, pattern recognition, machine learning and many more.