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The kernel least-mean-square (KLMS) algorithm is an appealing tool for online identification of nonlinear systems due to its simplicity and robustness. In addition to choosing a reproducing kernel and setting filter parameters, designing a KLMS adaptive filter requires to select a so-called dictionary in order to get a finite-order model. This dictionary has a significant impact on performance, and requires careful consideration. Theoretical analysis of KLMS as a function of dictionary settingdoi:10.1109/icassp.2014.6855006 dblp:conf/icassp/ChenGRB14 fatcat:2mtw4dcgj5barjt7hg5izfqczi