Add to ORKGThis paper compares a wide variety of neural network architectures applied in the context of black-box modeling for robotics and control. We compare six different architectural concepts and four activation functions, with over three hundred different models. Those models were applied to three robotics datasets to show the differences in performance between the architectures along with their limitations
International audienceNeural networks are powerful tools for black box system identification. Howeve...
Neural networks can have approximate multi-power, so in recent years they have been used widely and ...
Abstract:- The paper deals with on-line system identification for adaptive controller construction. ...
This paper compares a wide variety of neural network architectures applied in the context of black-b...
Neural Networks are non-linear black-box model structures, to be used with conventional parameter es...
International audienceNonlinear system identification tends to pro- vide highly accurate models thes...
We analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neur...
In this report some examples on system identification of non-linear systems with neural networks are...
This report concerns the research topic of black box nonlinear system identification. In effect, amo...
The authors review some of the basic system identification machinery to reveal connections with neur...
AbstractMathematical modelling is used routinely to understand the coding properties and dynamics of...
Artificial neural networks (ANNs) have been used in the solution of a variety of mechanical system d...
Neural network black-box modeling is usually performed using nonlinear input-output models. The goal...
Artificial neural networks are empirical models which adjust their internal parameters, using a suit...
Neural network black-box modeling is usually performed using nonlinear inputoutput models. The goal ...
International audienceNeural networks are powerful tools for black box system identification. Howeve...
Neural networks can have approximate multi-power, so in recent years they have been used widely and ...
Abstract:- The paper deals with on-line system identification for adaptive controller construction. ...
This paper compares a wide variety of neural network architectures applied in the context of black-b...
Neural Networks are non-linear black-box model structures, to be used with conventional parameter es...
International audienceNonlinear system identification tends to pro- vide highly accurate models thes...
We analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neur...
In this report some examples on system identification of non-linear systems with neural networks are...
This report concerns the research topic of black box nonlinear system identification. In effect, amo...
The authors review some of the basic system identification machinery to reveal connections with neur...
AbstractMathematical modelling is used routinely to understand the coding properties and dynamics of...
Artificial neural networks (ANNs) have been used in the solution of a variety of mechanical system d...
Neural network black-box modeling is usually performed using nonlinear input-output models. The goal...
Artificial neural networks are empirical models which adjust their internal parameters, using a suit...
Neural network black-box modeling is usually performed using nonlinear inputoutput models. The goal ...
International audienceNeural networks are powerful tools for black box system identification. Howeve...
Neural networks can have approximate multi-power, so in recent years they have been used widely and ...
Abstract:- The paper deals with on-line system identification for adaptive controller construction. ...