Nonlinear dynamic identification using supervised neural gas algorithm

Abstract

The dynamic identification of a nonlinear plant is not a trivial issue. The application of a neural gas network that is trained with a supervised batch version of the algorithm can produce identification models in a robust way. In this paper, the neural model identifies each local transfer function demonstrating that the local linear approximation can be done. Moreover, other parameters are analyzed in order to obtain a correct modeling.