Epistemology

Algorithm

Identification (biology)

Physics

Reservoir computing

Philosophy

Recurrent neural network

Artificial intelligence

Nonlinear system

Echo state network

Property (philosophy)

Control (management)

Echo (communications protocol)

Computer network

Computer science

Control theory (sociology)

State (computer science)

Artificial neural network

Botany

Quantum mechanics

Biology

Neural Networks

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.

Prediction and identification of discrete-time dynamic nonlinear systems based on adaptive echo state network