A generalization of the linear least squares method to a
wide class of parametric nonlinear inverse problems is presented.
The approach is based on the consideration of the operator
equations, with the selected function of parameters as the
solution. The generalization is based on the two mandatory
conditions: the operator equations are linear for the estimated
parameters and the operators have discrete approximations. Not
requiring use of iterations, this approach is well suited for
hardware implementation and also for constructing the first
approximation for the nonlinear least squares method. The examples of
parametric problems, including the problem of estimation of parameters
of some higher transcendental functions, are presented.