In order to apply a new dynamic neural network- Diagonal Recurrent Neural NetWork (DRNN) to the system identificationof nonlinear dynamic Systems and construct more accurate system models, the structure and learning m...In order to apply a new dynamic neural network- Diagonal Recurrent Neural NetWork (DRNN) to the system identificationof nonlinear dynamic Systems and construct more accurate system models, the structure and learning method (DBP algorithm) of theDRNN are Present6d. Nonlinear system characteriStics can be identified by presenting a set of input / output patterns tO the DRNN andadjusting its weights with the DBP algorithm. Experimental results show that the DRNN has good performances in the identification ofnonlinear dynamic systems in comparison with BP networks.展开更多
文摘In order to apply a new dynamic neural network- Diagonal Recurrent Neural NetWork (DRNN) to the system identificationof nonlinear dynamic Systems and construct more accurate system models, the structure and learning method (DBP algorithm) of theDRNN are Present6d. Nonlinear system characteriStics can be identified by presenting a set of input / output patterns tO the DRNN andadjusting its weights with the DBP algorithm. Experimental results show that the DRNN has good performances in the identification ofnonlinear dynamic systems in comparison with BP networks.
