  A non linear regression approach which consists of a specific regression
model incorporating a latent process, allowing various polynomial regression
models to be activated preferentially and smoothly, is introduced in this
paper. The model parameters are estimated by maximum likelihood performed via a
dedicated expecation-maximization (EM) algorithm. An experimental study using
simulated and real data sets reveals good performances of the proposed
approach.
