Neural-network-based cooperative adaptive identification of nonlinear systems

This paper considers the problem of cooperative adaptive identification for a class of nonlinear systems via neural networks. The proposed adaptive laws of neural network weights are distributed, and the interconnection topologies are established among identification models in order to share their data on-line. It is proved that if the interconnection topologies are undirected and connected, then all adaptive laws of neural network weights for the same system function can converge to a small neighborhood around their optimal values over a union of sets consisting of system trajectories. Thus, the learned system model has the better generalization capability. A simulation example are provided to verify the effectiveness and advantages of the algorithms proposed in this paper.