Neural networks have attracted much attention as a means of modelling
nonlinear phenomena, for example for inferential measurement in proces
s control. A neural network is a nonlinear multivariable function whos
e main potential advantage: the ability to represent highly nonlinear
input-output relationships, is in fact not essential in many potential
process engineering applications. This advantage is in any case frequ
ently outweighed by its major disadvantage: the intractability of the
parameter estimation procedure resulting from the highly nonlinear for
m of its parameters. In this work we show how moderately nonlinear fun
ctions, with easily estimated parameters may be used in certain infere
ntial measurement applications for which neural networks have been pro
posed. These functions are as effective in representing input-output r
elationships and their parameters can be fitted far more rapidly than
can the ''weights'' of a neural network. Furthermore, we show that the
performance of both these functions and neural networks, being arbitr
ary representations having no physical basis, may almost invariably be
improved upon by the use of even very simple approximate models based
on proper physical understanding.