Identification of discrete linear system in state space form using neural network
Dali Wang, Ali Zilouchian · 2002
A novel supervised recurrent neural network architecture for identification of discrete linear systems is introduced. The proposed neural network architecture directly provides the state space parameters of a given system based upon input-output data available. The simulation experiments demonstrate the effectiveness of the proposed method for both single input single output (SISO) and multi-input multi-output (MIMO) systems.