Generalized spline nonlinear adaptive filters. (15th October 2017)

• Record Type: Journal Article
• Authors: Rathod, Milan; Patel, Vinal; George, Nithin V.
• Is Part Of: Expert systems with applications. Volume 83(2017)
• Page Start: 122
• Page End: 130
• Publication Date: 2017-10-15
• Subjects: Adaptive filters -- Classification -- Functional link neural network -- Dynamic system identification -- Spline functions -- Steady state analysis; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.04.043
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A new spline based generalized non-linear filter. Adaptive spline function is used as basis function. Provides improved classification accuracy. Successfully applied in dynamic system identification. Abstract: A new nonlinear filter, which employs an adaptive spline function as the basis function is designed in this paper. The input signal to this filter is used to generate suitable parameters to update the control points in a spline function. The update rule for updating the control points have been derived and a mean square analysis has been carried out. The output of the spline functions are suitably combined together to obtain the filter response. This filter is called the generalized spline nonlinear adaptive filter (GSNAF). The proposed GSNAF is similar to a functional link artificial neural network (FLANN), considering a functional expansion using spline basis functions. GSNAF has been shown to offer improved accuracy in benchmark classification scenarios and provide enhanced modeling accuracy in single input single output as well as in multiple input multiple output dynamic system identification cases.