SINGLE-LAYER NEURAL NETWORKS FOR LINEAR-SYSTEM IDENTIFICATION USING GRADIENT DESCENT TECHNIQUE

Recently, some researchers have focused on the applications of neural networks for the system identification problems. In this letter we describe how to use the gradient descent (GD) technique with single layer neural networks (SLNN's) to identify the parameters of a linear dynamical system whose states and derivatives of state are given. It is shown that the use of the GD technique for the purpose of system identification of a linear time invariant dynamical system is simpler and less expensive in implementation because it involves less hardware than the technique using the Hopfield network as discussed by Chu. The circuit is considered to bc faster and is recommended for on-line computation because of the parallel nature of its architecture and the possibility of the use of analog circuit components. A mathematical formulation of the technique is presented and the simulation results of the network are included.