System identification using multilayer differential neural networks: A new result

In previous works,a learning law with a dead zone function was developed for multilayer differential neural networks. this scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. in this paper,the learning law is modified in such a way that this condition is relaxed. by this modification,the tuning process is simpler and the dead-zone function is not required anymore. on the basis of this modification and by using a lyapunov-like analysis,a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. besides,a value for upper bound of such zone is provided. the workability of this approach is tested by a simulation example. copyright © 2012 j. humberto pérez-cruz et al.