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The redundancy-removed matrix-form representation given by (19) can also be used for obtaining a reduced-rank implementation.
Then, such a strategy is exploited along with the singular value decomposition to obtain the proposed reduced-rank implementation approach.
Then, applying second-order reduced-rank implementation strategies to such a structure, effective reduced-rank implementations for higher-order Volterra filters can be achieved.
Additionally, the fuzzy-logic-controlled matrix conjugate gradient (MCG) algorithm is developed for a robust and reduced-rank implementation of the full-rank MMWF.
Taking into account this fact, a reduced-rank implementation for the pth-order kernel can be obtained by applying, to each second-order kernel resulting from the kernel decomposition, the strategy used for obtaining the reduced-rank implementation of Fig. 1 (see Section 2.3).
Then, exploiting the singular value decomposition along with the coefficient matrices of these second-order kernels, a reduced-rank implementation for the higher-order Volterra filter can be achieved.
Fig. 4 Number of coefficients required by different reduced-rank implementations of a third-order kernel.
Also, in Section 2, the reduced-rank implementations of Volterra filters are briefly described.
iii) Obtain reduced-rank implementations of such second-order kernels by using the singular value decomposition as described in Section 2.3.
This type of representation is especially suited for the development of reduced-rank implementations, as will be shown in the next section.
For higher-order filters, matrix-based reduced-rank approaches are usually obtained by considering non-trivial definitions of (often rectangular) coefficient matrices [24], which occasionally lead to ineffective reduced-rank implementations.
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