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- W2146159356 abstract "We consider the application of kernel canonical correlation analysis (K-CCA) to the supervised equalization of Wiener systems. Although a considerable amount of research has been carried out on identification/equalization of Wiener models, in this paper we show that K-CCA is a particularly suitable technique for the inversion of these nonlinear dynamic systems. Another contribution of this paper is the development of an online K-CCA algorithm which combines a sliding-window approach with a recently proposed reformulation of CCA as an iterative regression problem. This online algorithm permits fast equalization of time-varying Wiener systems. Simulation examples are added to illustrate the performance of the proposed method." @default.
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- W2146159356 date "2006-01-01" @default.
- W2146159356 modified "2023-10-03" @default.
- W2146159356 title "Online Kernel Canonical Correlation Analysis for Supervised Equalization of Wiener Systems" @default.
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- W2146159356 doi "https://doi.org/10.1109/ijcnn.2006.246827" @default.
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