Solving assignment problems with chaotic neural networks

Assignment problems are solved by a chaos neural network (CNN) in which a pair of coupled chaos oscillators act as a neuron. Compared with the conventional Hopfield neural networks (HNN), CNN can be expected to have higher ability of searching for globally optimal or near-optimal solution to assignment problems. Numerical simulations of assignment problems in a real-time distributed system show that CNN can overcome HNN's main drawbacks that suffer from the local minimum and perform even more efficient searching when to solve assignment problems.