Proportionate-type algorithms are designed to exploit the sparseness character of the systems to be identified, in order to improve the overall convergence of the adaptive filters used in this context. However, when t...
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ISBN:
(纸本)9781538646953
Proportionate-type algorithms are designed to exploit the sparseness character of the systems to be identified, in order to improve the overall convergence of the adaptive filters used in this context. However, when the parameter space is large, the system identification problem becomes more challenging. In this paper, we focus on the identification of bilinear forms, where the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. In this framework, we develop a proportionate normalized least-mean-square algorithm tailored for the identification of such bilinear forms. Simulation results indicate the good performance of the proposed algorithm, in terms of both convergence rate and computational complexity.
The identification of bilinear forms is a challenging problem since its parameter space may be very large and the adaptive filters should be able to cope with this aspect. Recently, the recursive least-squares tailore...
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ISBN:
(纸本)9789082797015
The identification of bilinear forms is a challenging problem since its parameter space may be very large and the adaptive filters should be able to cope with this aspect. Recently, the recursive least-squares tailored for bilinear forms (namely RLS-BF) was developed in this context. In order to reduce its computational complexity, two versions based on the dichotomous coordinate descent (DCD) method are proposed in this paper. Simulation results indicate the good performance of these algorithms, with appealing features for practical implementations.
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