State and parameter estimations of chaotic memristive systems based on extended Kalman filter

This paper addresses the problem of estimating states and parameters of chaotic memristive systems with single measurable variable. Based on the conventional extended Kalman filter, we present two new strategies based on the joint extended Kalman filter and dual extended Kalman filter. Then, the two kinds of filters are employed to estimate the states and parameters of chaotic memristive systems. Finally, numerical simulations are provided to illustrate the effectiveness of the proposed methods.