System Identification Using Multilayer Differential Neural Networks: A New Result

Abstract EN

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.