This paper presents a new recurrent neural network (RNN) structure called ENEM for dynamic system identification. ENEM structure is based on Elman network and NARX neural network. In order to show the performance of ENEM for system identification, the results were also compared to the results of Elman network, Jordan network and their modified models. The identification results of linear and nonlinear systems have shown that the proposed ENEM structure is better than the other results of RNN models
Multi-layered neural networks offer an exciting alternative for modelling complex non-linear systems...
Digital Object Identifier : 10.1109/NNSP.1991.239489The authors describe a special type of dynamic ...
Two approaches are presented to calculate the weights for a Dynamic Recurrent Neural Network (DRNN) ...
This paper presents a new recurrent neural network (RNN) structure called ENEM for dynamic system id...
This paper introduces a new approach based on artificial neural networks (ANNs) to identify a number...
This paper puts forward a novel recurrent neural network (RNN), referred to as the context layered l...
In this paper, we study an induction motor identification in all states and conditions whether trans...
This paper presents a type of recurrent artificial neural network architecture for identification of...
Methods for model identification are crucial in many fields, such as adaptive signal processing, pat...
This paper presents a scrutinized investigation on system identification using artificial neural net...
This paper presents a Hammerstein-Wiener recurrent neural network (HWRNN) with a systematic identifi...
In this study, the application of Recurrent Artificial Neural Network (RANN) in nonlinear system ide...
Currently, almost all efforts for using artificial neural networks for control oriented process iden...
This paper discusses memory neuron networks as models for identification and adaptive control of non...
Neural Networks are non-linear black-box model structures, to be used with conventional parameter es...
Multi-layered neural networks offer an exciting alternative for modelling complex non-linear systems...
Digital Object Identifier : 10.1109/NNSP.1991.239489The authors describe a special type of dynamic ...
Two approaches are presented to calculate the weights for a Dynamic Recurrent Neural Network (DRNN) ...
This paper presents a new recurrent neural network (RNN) structure called ENEM for dynamic system id...
This paper introduces a new approach based on artificial neural networks (ANNs) to identify a number...
This paper puts forward a novel recurrent neural network (RNN), referred to as the context layered l...
In this paper, we study an induction motor identification in all states and conditions whether trans...
This paper presents a type of recurrent artificial neural network architecture for identification of...
Methods for model identification are crucial in many fields, such as adaptive signal processing, pat...
This paper presents a scrutinized investigation on system identification using artificial neural net...
This paper presents a Hammerstein-Wiener recurrent neural network (HWRNN) with a systematic identifi...
In this study, the application of Recurrent Artificial Neural Network (RANN) in nonlinear system ide...
Currently, almost all efforts for using artificial neural networks for control oriented process iden...
This paper discusses memory neuron networks as models for identification and adaptive control of non...
Neural Networks are non-linear black-box model structures, to be used with conventional parameter es...
Multi-layered neural networks offer an exciting alternative for modelling complex non-linear systems...
Digital Object Identifier : 10.1109/NNSP.1991.239489The authors describe a special type of dynamic ...
Two approaches are presented to calculate the weights for a Dynamic Recurrent Neural Network (DRNN) ...