A review: state estimation based on hybrid models of Kalman filter and neural network

Abstract

ABSTRACT In this paper, hybrid models of Kalman filter and neural network for state estimation are reviewed of their corresponding academic achievements, the creation of which is a noteworthy development in state estimation. This paper aims to provide a summary of research progress on such hybrid models and emphasize their functions and advantages. First of all, the concept and feature are paid attention to about Kalman filter, and its transmutative modes are taken into consideration. Then several popular neural network algorithms are introduced in brief. Subsequently, research results on hybrid models are analysed and discussed comprehensively. Not only Kalman filter and neural network can be adopted in succession, but also can be mixed in structure. The mixed models can also be divided into two types, the equations or parameters of state–space model are trained by neural network for Kalman filter and the parameters of neural network are updated by Kalman filter. It is proved that the hybrid models outperform than single model of Kalman filter or neural network in accuracy and generalization. Last but not least, the effectiveness of state–space equations of Kalman filter can be established by neural network in nonlinear systems is verified.