A neural network model for solving the N-Queens problem is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of the N-Queens problem. Simulation results are presented to validate the proposed approach
WOS: A1996VL22600006This paper introduces a new family of multivalued neural networks. We have inter...
This paper presents a study on the N-Queens Problem. Different approaches to its solution discussed ...
Abstract: Multi-modal optimisation problems are characterised by the presence of either local sub-op...
Neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear proce...
A model of neurons with CHN (Continuous Hysteresis Neurons) for the Hopfield neural networks is stud...
Neural networks can be successfully applied to solving certain types of combinatorial optimization p...
This paper presents an efficient approach based on a recurrent neural network for solving constraine...
Neural networks consist of highly interconnected and parallel nonlinear processing elements that are...
This paper presents an efficient approach based on recurrent neural network for solving nonlinear op...
Abstract. In the use of Hopfield networks to solve optimization problems, a critical problem is the ...
Systems based on artificial neural networks have high computational rates owing to the use of a mass...
The paper discusses the implementation of Hopfield neural networks for solving constraint satisfacti...
Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonl...
Combinatorial optimization is an active field of research in Neural Networks. Since the first attemp...
This research proposes the swapping algorithm a new algorithm for solving the n-queens problem, and ...
WOS: A1996VL22600006This paper introduces a new family of multivalued neural networks. We have inter...
This paper presents a study on the N-Queens Problem. Different approaches to its solution discussed ...
Abstract: Multi-modal optimisation problems are characterised by the presence of either local sub-op...
Neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear proce...
A model of neurons with CHN (Continuous Hysteresis Neurons) for the Hopfield neural networks is stud...
Neural networks can be successfully applied to solving certain types of combinatorial optimization p...
This paper presents an efficient approach based on a recurrent neural network for solving constraine...
Neural networks consist of highly interconnected and parallel nonlinear processing elements that are...
This paper presents an efficient approach based on recurrent neural network for solving nonlinear op...
Abstract. In the use of Hopfield networks to solve optimization problems, a critical problem is the ...
Systems based on artificial neural networks have high computational rates owing to the use of a mass...
The paper discusses the implementation of Hopfield neural networks for solving constraint satisfacti...
Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonl...
Combinatorial optimization is an active field of research in Neural Networks. Since the first attemp...
This research proposes the swapping algorithm a new algorithm for solving the n-queens problem, and ...
WOS: A1996VL22600006This paper introduces a new family of multivalued neural networks. We have inter...
This paper presents a study on the N-Queens Problem. Different approaches to its solution discussed ...
Abstract: Multi-modal optimisation problems are characterised by the presence of either local sub-op...