Add to ORKGIn this study, we investigate two different update methods of the chaos neural network in order to approach to finding the global minimum solution of TSPs. By computer simulations for various TSPs, we confirm that the method to change the weights connecting the firing neu-rons can successfully restrain the regeneration of the once-appeared-solutions. 1
Summary. Traditional Pattern Recognition (PR) systems work with the model that the object to be reco...
In this paper,a definite relation between the TSP's optimal solution and the attracting region ...
Abstract: A new neural network based optimization algorithm is proposed.The presented model is a dis...
In the optimization problems, many algorithms pour-ing chaotic oscillations to the Neural Networks (...
We review the approaches for solving combinatorial optimization problems by chaotic dynamics. We men...
A neural network is a model of the brain’s cognitive process, with a highly interconnected multiproc...
A single particle structure of particle swarm optimization was analyzed which is found to have some ...
tructure of a strange attractor in the phase space without getting stuck at local minima. This abili...
Abstract — In the area of artificial neural networks, the Back Propagation (BP) learning algorithm h...
A novel modified method for obtaining approximate solutions to difficult optimization problems withi...
Although it would be possible to solve combinatorial op-timization problems with a huge number of el...
The main advantage of detecting chaos is that the time series is short term predictable. The predict...
In this paper, we investigate the solving ability of Hop-field neural network with iterative simulat...
Chaos and fractals are novel fields of physics and mathematics showing up a new way of universe view...
Neural networks can be successfully applied to solving certain types of combinatorial optimization p...
Summary. Traditional Pattern Recognition (PR) systems work with the model that the object to be reco...
In this paper,a definite relation between the TSP's optimal solution and the attracting region ...
Abstract: A new neural network based optimization algorithm is proposed.The presented model is a dis...
In the optimization problems, many algorithms pour-ing chaotic oscillations to the Neural Networks (...
We review the approaches for solving combinatorial optimization problems by chaotic dynamics. We men...
A neural network is a model of the brain’s cognitive process, with a highly interconnected multiproc...
A single particle structure of particle swarm optimization was analyzed which is found to have some ...
tructure of a strange attractor in the phase space without getting stuck at local minima. This abili...
Abstract — In the area of artificial neural networks, the Back Propagation (BP) learning algorithm h...
A novel modified method for obtaining approximate solutions to difficult optimization problems withi...
Although it would be possible to solve combinatorial op-timization problems with a huge number of el...
The main advantage of detecting chaos is that the time series is short term predictable. The predict...
In this paper, we investigate the solving ability of Hop-field neural network with iterative simulat...
Chaos and fractals are novel fields of physics and mathematics showing up a new way of universe view...
Neural networks can be successfully applied to solving certain types of combinatorial optimization p...
Summary. Traditional Pattern Recognition (PR) systems work with the model that the object to be reco...
In this paper,a definite relation between the TSP's optimal solution and the attracting region ...
Abstract: A new neural network based optimization algorithm is proposed.The presented model is a dis...