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- W2044854966 abstract "technique to adaptive nonlinear filtering for one-dimensional (1-D) signals.2. INTRODUCTIONSignal processing suffers from the lack of elegant unified theory of nonlinear filtering. Theefforts to generalize the formalism of linear filtering theory onto the nonlinear case failed so far.Nonetheless rich ensembles of nonlinear filters have been studied in details and their performanceand robustness in many particular cases is acknowledged to be better than linear ones. There arealso some general conclusions of qualitative nature concerning the application of nonlinear filters.The problem we are strongly interested in is the optimal filtering, i.e., the task to pick up from thefamily of given nonlinear filters the best performing one. There is strong research interest for theadaptive nonlinear filtering because the solutions for nonlinear optimal filtering exist in the closedform in very limited number of cases. In this paper the genetic algorithm is applied to solve theadaptive filtering problem over the class of nonlinear filters so called neural filters. Neural filtersare the broad class of nonlinear filters that contains many families of filters e.g., stack filters as itssubclass. The results valid for neural filters therefore apply to wide area of nonlinear filtering. Thecomputational simulations support the applicability of genetic algorithm approach to adaptive" @default.
- W2044854966 created "2016-06-24" @default.
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- W2044854966 date "1995-03-01" @default.
- W2044854966 modified "2023-09-23" @default.
- W2044854966 title "<title>Adaptive filter design using genetic algorithm</title>" @default.
- W2044854966 doi "https://doi.org/10.1117/12.203229" @default.
- W2044854966 hasPublicationYear "1995" @default.
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