Cross-coupled Hopfield nets via generalized-delta-rule-based internetworks

An integrated neural network architecture is proposed in which two Hopfield networks are cross-coupled via multilayered internetworks. A Lyapunov function for storing one state in each Hopfield network leads to the necessity of the delta rule for training two-layered linear internetworks. The generalized delta rule is also derived in the case of using multilayered internetworks with nonlinear hidden units. Each internetwork is composed of forward and backward subnetworks with the same connection weights. In the backward subnetworks, the deltas for connectionist learning are computed. At the same time, their final outputs and the inputs to them are utilized effectively for network relaxation via extra paths to Hopfield networks. Simulation in robotic motion control illustrates that the network can associate the smooth motion from a key configuration to the memorized one