VLSI array processors
On the stability of the travelling salesman problem algorithm of Hopfield and Tank
Biological Cybernetics
A neural network approach to the 3-satisfiability problem
Journal of Parallel and Distributed Computing - Neural Computing
The logic of activation functions
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Fundamentals of Computer Alori
Fundamentals of Computer Alori
A Hybrid Estimator for Selectivity Estimation
IEEE Transactions on Knowledge and Data Engineering
Hi-index | 14.99 |
A hybrid neural network model for solving optimization problems is proposed. An energy function which contains the constraints and cost criteria of an optimization problem is derived, and then the neural network is used to find the global minimum (or maximum) of the energy function, which corresponds to a solution of the optimization problem. The network contains two subnets: a constraint network and a goal network. The constraint network models the constraints of an optimization problem and computes the gradient (updating) value of each neuron such that the energy function monotonically converges to satisfy all constraints of the problem. The goal network points out the direction of convergence for finding an optimal value for the cost criteria. These two subnets ensure that the neural network finds feasible as well as optimal (or near-optimal) solutions. The traveling salesman problem and the Hamiltonian cycle problem are used to demonstrate the method.