  We propose a novel approach to nonlinear functional regression, called the
Mapping-to-Parameter function model, which addresses complex and nonlinear
functional regression problems in parameter space by employing any supervised
learning technique. Central to this model is the mapping of function data from
an infinite-dimensional function space to a finite-dimensional parameter space.
This is accomplished by concurrently approximating multiple functions with a
common set of B-spline basis functions by any chosen order, with their knot
distribution determined by the Iterative Local Placement Algorithm, a newly
proposed free knot placement algorithm. In contrast to the conventional
equidistant knot placement strategy that uniformly distributes knot locations
based on a predefined number of knots, our proposed algorithms determine knot
location according to the local complexity of the input or output functions.
The performance of our knot placement algorithms is shown to be robust in both
single-function approximation and multiple-function approximation contexts.
Furthermore, the effectiveness and advantage of the proposed prediction model
in handling both function-on-scalar regression and function-on-function
regression problems are demonstrated through several real data applications, in
comparison with four groups of state-of-the-art methods.
