DocumentCode
2466670
Title
Genetic Programming Based Multichannel Identification of Nonlinear Systems by Volterra Filters
Author
Yao, Leehter ; Lin, Chin-Chin
Author_Institution
Nat. Taipei Univ. of Technol., Taipei
fYear
0
fDate
0-0 0
Firstpage
2864
Lastpage
2871
Abstract
Genetic programming (GP) is utilized to search the optimal structure of Volterra filter in this paper. The Volterra filter with high order and large memories contains great amount of cross product terms. Instead of applying GP to search all cross products, GP is utilized to search a smaller set of primary signals which evolve to the whole set of cross products. With GP´s optimization capability, the important primary signals and the associated cross products of input signals attributing most to the outputs will be chosen while the primary signals and their associated cross products of input signals which are trivial to the outputs will be excluded from the possible candidate primary signals.
Keywords
genetic algorithms; nonlinear filters; nonlinear systems; Volterra filter; genetic programming; multichannel identification; nonlinear system; optimization capability; primary signals; Delay; Filter bank; Genetic programming; Kernel; Least squares approximation; Linear regression; Nonlinear filters; Nonlinear systems; Resonance light scattering; Signal processing;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
Conference_Location
Vancouver, BC
Print_ISBN
0-7803-9487-9
Type
conf
DOI
10.1109/CEC.2006.1688669
Filename
1688669
Link To Document