Parameter identification via neural networks with fast convergence

The parameter identification using artificial neural networks is becoming very popular. In this chapter, the parameters of dynamical system are identified using artificial neural networks. A fast gradient decent technique for the parameter identification of a linear dynamical system has been presented. The following concepts are used for training of neural networks while identifying the system parameters: (1) batch wise training of neural networks; (2) variable learning parameter and; (3) an intelligent check over the rate at which parameters are converging. The complete algorithm is summarized as a flow chart. A detailed mathematical formulation is given. The simulation results and a comparative study with existing method is included. (C) 2000 IMACS/Elsevier Science B.V. All rights reserved.