A neural network for hierarchical optimization of nonlinear large-scale systems

A neural scheme for hierarchical optimization of nonlinear large-scale systems is presented. The local optimization subnetworks and co-ordination subnetwork, comprising the hierarchical optimization neural network, work simultaneously to provide the optimal solutions to the original optimization problems, so the waiting time that exists in the processes of local optimization and co-ordination of conventional numerical methods is eliminated naturally. Moreover, local stability of the neural network is obtained with the aid of a convexification procedure, under which separability for the application of decomposition and coordination strategy is preserved. The neural network is efficient in solving large-scale optimization problems and is suitable for realtime applications.