Add to ORKGWe introduce a recurrent network architecture for modelling a general class of dynamical systems
A fully connected recurrent ANN model is proposed as a generator of stable limit cycles. A hybrid ge...
We investigate the predictive power of recurrent neural networks for oscillatory systems not only on...
This paper puts forward a novel recurrent neural network (RNN), referred to as the context layered l...
This paper presents a type of recurrent artificial neural network architecture for identification of...
The Recurrent Neural Networks (RNNs) represent an important class of bio-inspired learning machines ...
Dynamical systems have been used to describe a vast range of phenomena, including physical sciences...
In this paper, the problem of modeling and identification of complex input-output systems using recu...
This work provides a framework for the approximation of a dynamic system of the form x˙=f(x)+g(x)u b...
Ph.D.Thesis, Computer Science Dept., U Rochester; Dana H. Ballard, thesis advisor; simultaneously pu...
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the conn...
Recurrent neural network models with parallel distributed architecture are constructed using ordinar...
This paper introduces a new approach based on artificial neural networks (ANNs) to identify a number...
This thesis deals with the methodology of building data driven models of nonlinear systems through ...
Several learning algorithms have been derived for equilibrium points in recurrent neural networks. I...
Methods for model identification are crucial in many fields, such as adaptive signal processing, pat...
A fully connected recurrent ANN model is proposed as a generator of stable limit cycles. A hybrid ge...
We investigate the predictive power of recurrent neural networks for oscillatory systems not only on...
This paper puts forward a novel recurrent neural network (RNN), referred to as the context layered l...
This paper presents a type of recurrent artificial neural network architecture for identification of...
The Recurrent Neural Networks (RNNs) represent an important class of bio-inspired learning machines ...
Dynamical systems have been used to describe a vast range of phenomena, including physical sciences...
In this paper, the problem of modeling and identification of complex input-output systems using recu...
This work provides a framework for the approximation of a dynamic system of the form x˙=f(x)+g(x)u b...
Ph.D.Thesis, Computer Science Dept., U Rochester; Dana H. Ballard, thesis advisor; simultaneously pu...
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the conn...
Recurrent neural network models with parallel distributed architecture are constructed using ordinar...
This paper introduces a new approach based on artificial neural networks (ANNs) to identify a number...
This thesis deals with the methodology of building data driven models of nonlinear systems through ...
Several learning algorithms have been derived for equilibrium points in recurrent neural networks. I...
Methods for model identification are crucial in many fields, such as adaptive signal processing, pat...
A fully connected recurrent ANN model is proposed as a generator of stable limit cycles. A hybrid ge...
We investigate the predictive power of recurrent neural networks for oscillatory systems not only on...
This paper puts forward a novel recurrent neural network (RNN), referred to as the context layered l...