Generalized spline nonlinear adaptive filters |
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| Affiliation: | 1. College of Computer Science and Technology, University South China, Hengyang 421001, China;2. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;1. Instituto Superior Politécnico José Antonio Echeverría, Calle 114 No. 11901, Marianao, La Habana C.P. 19390, Cuba;2. Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro No. 1, Sta. María Tonanzintla, Puebla C.P. 72840, México;3. Centro de Bioplantas, Universidad de Ciego de Ávila, Carretera a Morón km 9, Ciego de Ávila C.P. 69450, Cuba;1. Department of Software Engineering, University of Granada, 18071 Granada, Spain;2. Department of Marketing and Market Research, Complutense University of Madrid, 28015 Madrid, Spain;3. Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain;4. Department of Electrical and Computer Engineering, King Abdulaziz University, 21589 Jeddah, Saudi Arabia;5. Centre for Computational Intelligence, De Montfort University, LE1 9BH Leicester, UK;1. Department of Civil Engineering, New Mexico State University, MSC 3CE, PO Box 30001, Las Cruces, NM, USA, 88003;2. Texas AgriLife Research & Extension Center at El Paso, Texas A&M University System, 1380 A&M Circle, El Paso, TX 79927, USA;1. Chair of Energy Economics, Karlsruhe Institute of Technology (KIT), Hertzstr. 16, 76187 Karlsruhe, Germany;2. Economic and Social Research Institute & Trinity College Dublin, Whitaker Square, Sir John Rogerson''s Quay, Dublin 2, Ireland |
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| 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. |
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