Abstract
The structures of the four representative dynamic neural networks (NN) are presented. In order to compare the performance of different dynamic NN in the nonlinear system identification, they are used for identification of the same nonlinear dynamic system, using the genetic algorithm (GA) to train the weights of the Elman net, the modified Elman net, internal time-delayed recurrent NN and time-delayed NN. The simulation results show the generalization ability of the four dynamic NN and provide the high precision of model of the nonlinear dynamic system. It illustrates the advantages and disadvantages of the different dynamic NN.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
