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In this paper, we consider the characteristics of the kernel adaptive filters for the mixture of linear and non-linear environments. We first consider employing a linear kernel as one of the kernels in multi-kernel adaptive filters. It is pointed out that the convergence characteristics of the filter corresponding to the linear kernel is affected by the selection of the other kernels. Then, we propose...
A new radial-angular- adaptive singularity cancellation transformation is proposed. This new transformation is flexible and applicable to singular integrals over triangular domains. When the height tends to zero and the observation point is in the plane of the source domain, the transformation remains stable and approaches the corresponding transformation within the source plane. Usually...
This paper presents an iterative classification algorithm called Ridge-adjusted Slack Variable Optimization (RiSVO). RiSVO is an iterative procedure with two steps: (1) A working subset of the training data is selected so as to reject “extreme” patterns. (2) the decision vector and threshold value are obtained by minimizing the energy function associated with the slack variables. From a computational...
This work introduces a framework devoted to the design of parametric estimators with very fast convergence properties for continuous-time dynamic systems characterized by bounded relative degree and possibly affected by structured perturbations. More specifically, the design of suitable kernels of non-anticipative linear integral operators gives rise to estimators that are ideally not influenced by...
In this paper, we propose a mixture structure of the linear and kernel adaptive fiilters for improving the convergence characteristics of the kernel normalized least mean square (KLMS) adaptive algorithm. The proposed method is based on the concept of the affine constrained mixture structure for the linear normalized LMS adaptive filters which uses the more than two adaptive filters concurrently....
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