Deep Boltzmann machine for nonlinear system modelling

Abstract

Deep Boltzmann machine (DBM) has been successfully applied in classification, regression and time series modeling. For nonlinear system modelling, DBM should also have many advantages over the other neural networks, such as input features extraction and noise tolerance. In this paper, we use DBM to model nonlinear systems by calculating the probability distributions of the input and output. Two novel weight updating algorithms are proposed to obtain these distributions. We use binary encoding and conditional probability transformation methods. The proposed methods are validated with two benchmark nonlinear systems.