This paper is concerned with utilizing neural networks and analog circuits to solve constrained optimization problems. A novel neural network architecture is proposed for solving a class of nonlinear programming problems. The proposed neural network, or more precisely a physically realizable approximation, is then used to solve minimum norm problems subject to linear constraints. Minimum norm problems have many applications in various areas, but we focus on their applications to the control of discrete dynamic processes. The applicability of the proposed neural network is demonstrated on numerical examples
A neural network model for solving constrained nonlinear optimization problems with bounded variable...
Artificial Neural Networks are a supervised machine learning technique with a number of drawbacks. T...
Constrained optimization problems entail the minimization or maximization of a linear or quadratic o...
This paper is concerned with utilizing neural networks and analog circuits to solve constrained opti...
This paper is concerned with utilizing neural networks and analog circuits to solve constrained opti...
This paper is concerned with utilizing analog circuits to solve various linear and nonlinear program...
The subject of this thesis is an application of artificial neural networks to solving linear and non...
This paper is concerned with neural networks which have the ability to solve linear and nonlinear co...
In this paper, neural networks for online solution of linear and nonlinear programming problems are ...
In this paper we consider several Neural Network architectures for solving constrained optimization ...
none5noThis brief proposes a neural network for the solution in real time of a class of quadratic op...
This paper presents a recurrent neural circuit for solving linear programming problems. The objectiv...
This paper presents an efficient approach based on a recurrent neural network for solving constraine...
Architectures and circuit techniques for implementing general piecewise constrained optimization pro...
We provide a practical and effective method for solving constrained optimization problems by success...
A neural network model for solving constrained nonlinear optimization problems with bounded variable...
Artificial Neural Networks are a supervised machine learning technique with a number of drawbacks. T...
Constrained optimization problems entail the minimization or maximization of a linear or quadratic o...
This paper is concerned with utilizing neural networks and analog circuits to solve constrained opti...
This paper is concerned with utilizing neural networks and analog circuits to solve constrained opti...
This paper is concerned with utilizing analog circuits to solve various linear and nonlinear program...
The subject of this thesis is an application of artificial neural networks to solving linear and non...
This paper is concerned with neural networks which have the ability to solve linear and nonlinear co...
In this paper, neural networks for online solution of linear and nonlinear programming problems are ...
In this paper we consider several Neural Network architectures for solving constrained optimization ...
none5noThis brief proposes a neural network for the solution in real time of a class of quadratic op...
This paper presents a recurrent neural circuit for solving linear programming problems. The objectiv...
This paper presents an efficient approach based on a recurrent neural network for solving constraine...
Architectures and circuit techniques for implementing general piecewise constrained optimization pro...
We provide a practical and effective method for solving constrained optimization problems by success...
A neural network model for solving constrained nonlinear optimization problems with bounded variable...
Artificial Neural Networks are a supervised machine learning technique with a number of drawbacks. T...
Constrained optimization problems entail the minimization or maximization of a linear or quadratic o...
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