Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ
Tải xuống
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Adaptive Kernel Canonical Correlation Analysis Algorithms for Nonparametric Identification of Wiener and Hammerstein Systems | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 875351 13 pages doi 10.1155 2008 875351 Research Article Adaptive Kernel Canonical Correlation Analysis Algorithms for Nonparametric Identification of Wiener and Hammerstein Systems Steven Van Vaerenbergh Javier Via and Ignacio Santamaria Department of Communications Engineering University of Cantabria 39005 Santander Cantabria Spain Correspondence should be addressed to Steven Van Vaerenbergh steven@gtas.dicom.unican.es Received 1 October 2007 Revised 4 January 2008 Accepted 12 February 2008 Recommended by Sergios Theodoridis This paper treats the identification of nonlinear systems that consist of a cascade of a linear channel and a nonlinearity such as the well-known Wiener and Hammerstein systems. In particular we follow a supervised identification approach that simultaneously identifies both parts of the nonlinear system. Given the correct restrictions on the identification problem we show how kernel canonical correlation analysis KCCA emerges as the logical solution to this problem. We then extend the proposed identification algorithm to an adaptive version allowing to deal with time-varying systems. In order to avoid overfitting problems we discuss and compare three possible regularization techniques for both the batch and the adaptive versions of the proposed algorithm. Simulations are included to demonstrate the effectiveness of the presented algorithm. Copyright 2008 Steven Van Vaerenbergh et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION In recent years a growing amount of research has been done on nonlinear system identification 1 2 . Nonlinear dynamical system models generally have a high number of parameters although many problems can be sufficiently well approximated .
