Prediction and identification of discrete time dynamic nonlinear systems based on adaptive echo state network
In this paper, a new prediction and identification method based on adaptive echo state network (AESN) is proposed to identify a class of discrete time dynamic nonlinear systems (DDNS) Firstly, according to the characteristics of input signals, the reservoir state update equation of AESN can be adaptively adjusted. In order to guarantee the echo state property of AESN, a sufficient condition for echo state property is given. Secondly, the reservoir parameters of AESN are optimized to improve the identification and prediction performance of AESN. Thirdly, an improved online output weights learning method based on historical reservoir state and output error is given. Finally, the effectiveness of the proposed method is verified by simulation examples.
Neural Networks