Fuzzy heterogeneous neural networks are recently introduced models
based on neurons accepting heterogeneous inputs (i.e. mixtures of
numerical and non-numerical information possibly with missing data)
with either crisp or imprecise character, which can be coupled with
classical neurons. This paper compares the effectiveness of this kind
of networks with time-delay and recurrent architectures that use
classical neuron models and training algorithms in a signal
forecasting problem, in the context of finding models of the central
nervous system controllers.