Evolutionary Deep Learning with Extended Kalman Filter for Effective Prediction Modeling and Efficient Data Assimilation

Abstract

With increasing concerns about infrastructure sustainability, ubiquitous sensing is an integral part of smart infrastructure in the context of smart cities. It generates large data sets containing hidden patterns and intelligence, which must be effectively extracted to derive actionable wisdom to support decision-making. Thus, it is imperative to develop intelligent data analytics to extract intelligence from data. Various data analysis methods have been developed in recent decades, but the lack of robustness and data assimilation features prevents the previously developed methods from yielding adequately accurate results for time-variant data sets over a long duration. This paper proposes an improved deep belief network (DBN), a deep machine learning model, which is integrated with genetic algorithms (GAs) and the extended Kalman filter (EKF) for effective predictive modeling and efficient data assimilation. The proposed method uses a genetic algorithm to optimize the configuration of the DBN for the given problem. Then the DBN is trained in two steps, namely pretraining layer by layer and fine-tuning with either a conventional back propagation (BP) algorithm, namely BP-DBN, or an EKF that is generalized with a new algorithm for calculating the Jacobian matrix for many-layer DBNs, namely EKF-DBN, which was tested together with BP-DBN and a recurrence neural network (RNN) on three real cases with and without data assimilation. The comparison results showed that the EKF-DBN outperforms BP-DBN and RNN in both computational efficiency and accuracy for predictive modeling. In addition, EKF-DBN generates the error covariance matrix that enables the calculation of prediction confidence interval. This can be used to detect the anomalies in a real system.