  Many scientific problems aim to evaluate the function fo relating variables u
and y measured from a physical phenomenon. An exact knowledge of fo cannot be
expected, and many methods are used for deriving approximate functions fa
giving small error fo-fa. It is shown that deriving functions fa having finite
error is not possible using measured data only, and hypothesis, i.e.,
assumptions, on the function fo and on the disturbances corrupting the
measurements must be introduced. Two main classes of assumptions are used in
the literature. The most widely used is the Parametric Stochastic class, where
fo is assumed to be a function depending on a vector p and disturbances are
stochastic variables. In last decades, assumptions belonging to Set Membership
class have been investigated, where fo is assumed to a smooth function with
bounded smoothness parameter and disturbances are bounded variables. At first,
it is shown that SM methods achieve "theoretical" properties (e.g., finding fa
with known and minimal error error, if assumptions hold) for general nonlinear
assumptions on function fo, while are achieved by PS methods under very
restricted assumptions on fo. Then, it is proved that no method can be found
for validating PS or SM assumptions, so that at best the chosen assumptions can
be falsified, i.e. recognized not true. No falsification test of PS assumptions
is known, for general nonlinear assumptions on fo. A falsification test of SM
assumptions is proposed. Finally, the new class of Physical Set Membership
assumptions is proposed, able to integrate the information provided by a
function fp obtained by a PS method, with a suitably designed SM method. It is
shown that PSM methods enjoy the strong results of SM methods on theoretical
properties and assumption falsification, and that, making use of the
information provided by fp, may be more accurate than using only SM
assumptions.
