Restricted Boltzmann Machine for Nonlinear System Modeling

Abstract

In this paper, we use a deep learning method, restricted Boltzmann machine, for nonlinear system identification. The neural model has deep architecture and is generated by a random search method. The initial weights of this deep neural model are obtained from the restricted Boltzmann machines. To identify nonlinear systems, we propose special unsupervised learning methods with input data. The normal supervised learning is used to train the weights with the output data. The modified algorithm is validated by modeling two benchmark systems.