In this paper the use of neural networks for the control of dynamical systems is considered. Both identification and feedback control aspects are discussed as well as the types of system for which neural networks can provide a useful technique. Multi-layer Perceptron and Radial Basis function neural network types are looked at, with an emphasis on the latter. It is shown how basis function centre selection is a critical part of the implementation process and that multivariate clustering algorithms can be an extremely useful tool for finding centres
International audienceThis paper deals with model predictive control synthesis which take benefits f...
International audienceThis paper deals with model predictive control synthesis which take benefits f...
International audienceThis paper deals with model predictive control synthesis which take benefits f...
This paper presents a first attempt to relate the experimental studies to theoretical developments a...
This paper presents a discussion of the applicability of neural networks in the identification and c...
AbstractModels for the identification and control of nonlinear dynamical systems using neural networ...
The paper develops important fundamental steps in applying artficial neural networks in the design o...
A review of W. Thomas Miller, III, Richard S. Sutton, and Paul J. Werbos (Eds.) Neural Networks for ...
The aim of this chapter is to introduce background concepts in nonlinear systems identification and...
The aim of this chapter is to introduce background concepts in nonlinear systems identification and...
The control of linear dynamical systems is a strategy that the brain uses to control its own intrins...
Representation of neural networks by dynamical systems is considered. The method of training of neur...
This paper discusses memory neuron networks as models for identification and adaptive control of non...
This paper discusses memory neuron networks as models for identification and adaptive control of non...
International audienceThis paper deals with model predictive control synthesis which take benefits f...
International audienceThis paper deals with model predictive control synthesis which take benefits f...
International audienceThis paper deals with model predictive control synthesis which take benefits f...
International audienceThis paper deals with model predictive control synthesis which take benefits f...
This paper presents a first attempt to relate the experimental studies to theoretical developments a...
This paper presents a discussion of the applicability of neural networks in the identification and c...
AbstractModels for the identification and control of nonlinear dynamical systems using neural networ...
The paper develops important fundamental steps in applying artficial neural networks in the design o...
A review of W. Thomas Miller, III, Richard S. Sutton, and Paul J. Werbos (Eds.) Neural Networks for ...
The aim of this chapter is to introduce background concepts in nonlinear systems identification and...
The aim of this chapter is to introduce background concepts in nonlinear systems identification and...
The control of linear dynamical systems is a strategy that the brain uses to control its own intrins...
Representation of neural networks by dynamical systems is considered. The method of training of neur...
This paper discusses memory neuron networks as models for identification and adaptive control of non...
This paper discusses memory neuron networks as models for identification and adaptive control of non...
International audienceThis paper deals with model predictive control synthesis which take benefits f...
International audienceThis paper deals with model predictive control synthesis which take benefits f...
International audienceThis paper deals with model predictive control synthesis which take benefits f...
International audienceThis paper deals with model predictive control synthesis which take benefits f...