RECURSIVE IDENTIFICATION OF HAMMERSTEIN SYSTEMS WITH POLYNOMIAL NONLINEARITIES
The paper deals with recursive identification of nonlinear dynamic systems using Hammerstein models with two-segment polynomial nonlinearities. A special form of Hammerstein model is considered, which is linear-in-parameters. The proposed algorithm is a direct application of the known recursive least squares method supplemented with the estimation of internal variables.
Summary form only given, as follows. A study is presented of precision constraints imposed by a hybrid chip architecture with analog neurons and digital backpropagation calculations. Conversions between the analog and digital domains and weight storage restrictions impose precision limits on both analog and digital calculations. It is shown through simulations that a learning system of this nature can be implemented in spite of limited resolution in the analog circuits and using fixed-point arithmetic to implement the backpropagation algorithm. >