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The present study proposes a new Orthogonal Floating Search framework forstructure selection of nonlinear systems by adapting the existing floatingsearch algorithms for feature selection. The proposed framework integrates theconcept of orthogonal space and consequent Error-Reduction-Ratio (ERR) metricwith the existing floating search algorithms. On the basis of this framework,three well-known feature selection algorithms have been adapted which includethe classical Sequential Forward Floating Search (SFFS), Improved sequentialForward Floating Search (IFFS) and Oscillating Search (OS). This frameworkretains the simplicity of classical Orthogonal Forward Regression with ERR(OFR-ERR) and eliminates the nesting effect associated with OFR-ERR. Theperformance of the proposed framework has been demonstrated considering severalbenchmark non-linear systems. The results show that most of the existingfeature selection methods can easily be tailored to identify the correct systemstructure of nonlinear systems.

Orthogonal Floating Search Algorithms: From The Perspective of Nonlinear System Identification