Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1713055 | Journal of Systems Engineering and Electronics | 2008 | 7 Pages |
Abstract
Anovel Hâ design methodology for a neural network-based nonlinear filtering scheme is addressed. Firstly, neural networks are employed to approximate the nonlinearities. Next, the nonlinear dynamic system is represented by the mode-dependent linear difference inclusion (LDI). Finally, based on the LDI model, a neural network-based nonlinear filter (NNBNF) is developed to minimize the upper bound of Hâ gain index of the estimation error under some linear matrix inequality (LMI) constraints. Compared with the existing nonlinear filters, NNBNF is time-invariant and numerically tractable. The validity and applicability of the proposed approach are successfully demonstrated in an illustrative example.
Related Topics
Physical Sciences and Engineering
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Authors
Luan Xiaoli, Liu Fei,