Identification of nonlinear systems by the genetic programming-based volterra filter

Created by W.Langdon from gp-bibliography.bib Revision:1.7810

@Article{Yao:2009:IETsp,

• author = "L. Yao and C.-C. Lin",
• title = "Identification of nonlinear systems by the genetic programming-based volterra filter",
• journal = "IET Signal Processing",
• year = "2009",
• month = mar,
• volume = "3",
• number = "2",
• pages = "93--105",
• keywords = "genetic algorithms, genetic programming, associated cross-products, genetic programming algorithm, input signal, nonlinear systems, optimal Volterra filter structure, reorganisation approach, tree extinction, tree pruning, nonlinear filters, nonlinear programming, signal processing",
• DOI = "doi:10.1049/iet-spr:20070203",
• ISSN = "1751-9675",
• pusblisher = "The Institution of Engineering and Technology",
• abstract = "The genetic programming (GP) algorithm is used to search for the optimal Volterra filter structure. A Volterra filter with high order and large memories contains a large number of cross-product terms. Instead of applying the GP algorithm to search for all cross-products of input signals, it is used to search for a smaller set of primary signals that evolve into the whole set of cross-products. With GP's optimisation, the important primary signals and the associated cross-products of input signals contributing most to the outputs are chosen whereas the primary signals and the associated cross-products of input signals that are trivial to the outputs are excluded from the possible candidate primary signals. To improve GP's learning capability, an effective directed initialisation scheme, a tree pruning and reorganisation approach, and a new operator called tree extinction and regeneration are proposed. Several experiments are made to justify the effectiveness and efficiency of the proposed modified by the GP algorithm.",
• notes = "IET, Also known as \cite{4784466}",

}

Genetic Programming entries for Leehter Yao Chin-chin Lin

Citations