Add to ORKGThis paper is concerned with utilizing analog circuits to solve various linear and nonlinear programming problems. The dynamics of these circuits are analyzed. Then, the previously proposed circuit implementations for solving optimization problems are examined. A new nonlinear programming network and its circuit implementation is then introduced which utilizes the nonlinearities to eliminate the problems encountered in previous circuit implementations
We propose and analyze two classes of neural network models for solving linear programming (LP) prob...
[[abstract]]This paper proposes a zero-order method of nonlinear optimization using back-propagation...
Both computer graphics and neural networks are related, in that they model natural phenomena. Physic...
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 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 ...
The subject of this thesis is an application of artificial neural networks to solving linear and non...
In this paper we consider several Neural Network architectures for solving constrained optimization ...
This paper presents a recurrent neural circuit for solving linear programming problems. The objectiv...
This paper explores whether analog circuitry can adequately perform constrained optimization. Const...
Architectures and circuit techniques for implementing general piecewise constrained optimization pro...
Constrained optimization problems entail the minimization or maximization of a linear or quadratic o...
A systematic approach for the design of analog neural nonlinear programming solvers using switched-c...
none5noThis brief proposes a neural network for the solution in real time of a class of quadratic op...
We propose and analyze two classes of neural network models for solving linear programming (LP) prob...
[[abstract]]This paper proposes a zero-order method of nonlinear optimization using back-propagation...
Both computer graphics and neural networks are related, in that they model natural phenomena. Physic...
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 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 ...
The subject of this thesis is an application of artificial neural networks to solving linear and non...
In this paper we consider several Neural Network architectures for solving constrained optimization ...
This paper presents a recurrent neural circuit for solving linear programming problems. The objectiv...
This paper explores whether analog circuitry can adequately perform constrained optimization. Const...
Architectures and circuit techniques for implementing general piecewise constrained optimization pro...
Constrained optimization problems entail the minimization or maximization of a linear or quadratic o...
A systematic approach for the design of analog neural nonlinear programming solvers using switched-c...
none5noThis brief proposes a neural network for the solution in real time of a class of quadratic op...
We propose and analyze two classes of neural network models for solving linear programming (LP) prob...
[[abstract]]This paper proposes a zero-order method of nonlinear optimization using back-propagation...
Both computer graphics and neural networks are related, in that they model natural phenomena. Physic...