Hammerstein uniform cubic spline adaptive filters: Learning and convergence properties

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• 作者：Michele Scarpiniti ; ^{michele.scarpiniti@uniroma1.it" class="auth_mail} ; Danilo Comminiello ^{danilo.comminiello@uniroma1.it" class="auth_mail} ; Raffaele Parisi ^{raffaele.parisi@uniroma1.it" class="auth_mail} ; Aurelio Uncini ^{aurelio.uncini@uniroma1.it" class="auth_mail}
• 关键词：Spline adaptive filter ; Nonlinear adaptive filter ; Least mean square ; Hammerstein system identification ; Excess mean square error
• 刊名：Signal Processing
• 出版年：July, 2014
• 年：2014
• 卷：100
• 期：Complete
• 页码：112-123
• 全文大小：1156 K

文摘

In this paper a novel class of nonlinear Hammerstein adaptive filters, consisting of a flexible memory-less function followed by a linear combiner, is presented. The nonlinear function involved in the adaptation process is based on a uniform cubic spline function that can be properly modified during learning. The spline control points are adaptively changed by using gradient-based techniques. This new kind of adaptive function is then applied to the input of a linear adaptive filter and it is used for the identification of Hammerstein-type nonlinear systems. In addition, we derive a simple form of the adaptation algorithm, an upper bound on the choice of the step-size and a lower bound on the excess mean square error in a theoretical manner. Some experimental results are also presented to demonstrate the effectiveness of the proposed method in the identification of high-order nonlinear systems.