NEURAL NODE, A NETWORK AND A CHAOTIC ANNEALING OPTIMIZATION METHOD FOR THE NETWORK

The present invention is a node for a network that combines a Hopfield and Tank type neuron, having a sigmoid type transfer function, with a nonmonotonic neuron, having a transfer function such as a parabolic transfer function, to produce a neural node with a deterministic chaotic response suitable for quickly and globally solving optimizatioin problems and avoiding local minima. The node can be included in a completely connected single layer network. The Hopfield neuron operates continuously while the nonmonotonic neuron operates periodically to prevent the network from getting stuck in a local optimum solution. The node can also be included in a local area architecture where local areas can be linked together in a hierarchy of nonmonotonic neurons.