Neural computing: theory and practice
Neural computing: theory and practice
Neural Networks and Simulation Methods
Neural Networks and Simulation Methods
Parameter setting of the Hopfield network applied to TSP
Neural Networks
On chaotic simulated annealing
IEEE Transactions on Neural Networks
Neural architectures optimization and genetic algorithms
WSEAS Transactions on Computers
Fast hopfield neural networks using subspace projections
Neurocomputing
The continuous hopfield networks (CHN) for the placement of the electronic circuits problem
WSEAS Transactions on Computers
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The continuous Hopfield network (CHN) is a classical neural network model. It can be used to solve some classification and optimization problems in the sense that the equilibrium points of a differential equation system associated to the CHN is the solution to those problems. The Euler method is the most widespread algorithm to obtain these CHN equilibrium points, since it is the simplest and quickest method to simulate complex differential equation systems. However, this method is highly sensitive with respect to initial conditions and it requires a lot of CPU time for medium or greater size CHN instances. In order to avoid these shortcomings, a new algorithm which obtains one equilibrium point for the CHN is introduced in this paper. It is a variable time-step method with the property that the convergence time is shortened; moreover, its robustness with respect to initial conditions will be proven and some computational experiences will be shown in order to compare it with the Euler method.