  The present study proposes a new Orthogonal Floating Search framework for
structure selection of nonlinear systems by adapting the existing floating
search algorithms for feature selection. The proposed framework integrates the
concept of orthogonal space and consequent Error-Reduction-Ratio (ERR) metric
with the existing floating search algorithms. On the basis of this framework,
three well-known feature selection algorithms have been adapted which include
the classical Sequential Forward Floating Search (SFFS), Improved sequential
Forward Floating Search (IFFS) and Oscillating Search (OS). This framework
retains the simplicity of classical Orthogonal Forward Regression with ERR
(OFR-ERR) and eliminates the nesting effect associated with OFR-ERR. The
performance of the proposed framework has been demonstrated considering several
benchmark non-linear systems. The results show that most of the existing
feature selection methods can easily be tailored to identify the correct system
structure of nonlinear systems.
