DOIAbstract
Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process
Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process
A two-neural network approach to solving nonlinear optimal control problems is described. This appro...
Purpose - To develop a new predictive control scheme based on neural networks for linear and non-lin...
A nonlinear extension of minimum variance and generalised minimum variance control strategies is dev...
This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to pre...
This paper presents a method for developing control laws for nonlinear systems based on an optimal c...
The modern stage of development of science and technology is characterized by a rapid increase in th...
This paper demonstrates a method to control a non-linear, multivariable, noisy process using trained...
Through the use of high-gain observer to estimate the unmeasurable system states, neural networks (N...
The topic of nonlinear control design has attracted particular attention to satisfy the demanding re...
ABSTRACT: Neural networks can be used to solve highly nonlinear control problems. This paper shows h...
A neural network predictive control scheme is compared with a first principle model predictive contr...
Infinite time optimal controllers have been designed for a dispersion type tubular reactor model by ...
The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an ...
In this paper the authors present two approaches for the control of an inverted pendulum on a cart. ...
A new optimal iterative neural network-based control (OINNC) strategy with simple computation and fa...
A two-neural network approach to solving nonlinear optimal control problems is described. This appro...
Purpose - To develop a new predictive control scheme based on neural networks for linear and non-lin...
A nonlinear extension of minimum variance and generalised minimum variance control strategies is dev...
This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to pre...
This paper presents a method for developing control laws for nonlinear systems based on an optimal c...
The modern stage of development of science and technology is characterized by a rapid increase in th...
This paper demonstrates a method to control a non-linear, multivariable, noisy process using trained...
Through the use of high-gain observer to estimate the unmeasurable system states, neural networks (N...
The topic of nonlinear control design has attracted particular attention to satisfy the demanding re...
ABSTRACT: Neural networks can be used to solve highly nonlinear control problems. This paper shows h...
A neural network predictive control scheme is compared with a first principle model predictive contr...
Infinite time optimal controllers have been designed for a dispersion type tubular reactor model by ...
The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an ...
In this paper the authors present two approaches for the control of an inverted pendulum on a cart. ...
A new optimal iterative neural network-based control (OINNC) strategy with simple computation and fa...
A two-neural network approach to solving nonlinear optimal control problems is described. This appro...
Purpose - To develop a new predictive control scheme based on neural networks for linear and non-lin...
A nonlinear extension of minimum variance and generalised minimum variance control strategies is dev...
Add to ORKG