. . "P\u0159\u00EDsp\u011Bvek se zab\u00FDv\u00E1 vyu\u017Eit\u00EDm adaptivn\u00EDch stochastick\u00FDch algoritm\u016F v~odhadu parametr\u016F neline\u00E1rn\u00EDch regresn\u00EDch model\u016F. Adaptivn\u00ED algoritmus \u0159\u00EDzen\u00E9ho n\u00E1hodn\u00E9ho prohled\u00E1v\u00E1n\u00ED (CRS) je experiment\u00E1ln\u011B porovn\u00E1n s~nov\u00FDmi adaptivn\u00EDmi verzemi diferenci\u00E1ln\u00ED evoluce (DE) na \u00FAloh\u00E1ch z~referen\u010Dn\u00ED datab\u00E1ze NIST. Pouze n\u011Bkter\u00E9 verze DE jen na n\u011Bkolika m\u00E1lo \u00FAloh\u00E1ch p\u0159ed\u010Dily algoritmus CRS."@cs . . "Adaptivn\u00ED stochastick\u00E9 algoritmy v neline\u00E1rn\u00ED regresi" . "8"^^ . . "RIV/61988987:17310/09:A1000QXQ!RIV10-MSM-17310___" . "Tvrd\u00EDk, Josef" . . "Nonlinear regression; parameter estimation; global optimization; differential evolution; controlled random search; self-adaptive algorithms."@en . "978-80-7015-004-7" . "Adaptivn\u00ED stochastick\u00E9 algoritmy v neline\u00E1rn\u00ED regresi"@cs . "Praha" . "[164316D3F30D]" . "P\u0159\u00EDsp\u011Bvek se zab\u00FDv\u00E1 vyu\u017Eit\u00EDm adaptivn\u00EDch stochastick\u00FDch algoritm\u016F v~odhadu parametr\u016F neline\u00E1rn\u00EDch regresn\u00EDch model\u016F. Adaptivn\u00ED algoritmus \u0159\u00EDzen\u00E9ho n\u00E1hodn\u00E9ho prohled\u00E1v\u00E1n\u00ED (CRS) je experiment\u00E1ln\u011B porovn\u00E1n s~nov\u00FDmi adaptivn\u00EDmi verzemi diferenci\u00E1ln\u00ED evoluce (DE) na \u00FAloh\u00E1ch z~referen\u010Dn\u00ED datab\u00E1ze NIST. Pouze n\u011Bkter\u00E9 verze DE jen na n\u011Bkolika m\u00E1lo \u00FAloh\u00E1ch p\u0159ed\u010Dily algoritmus CRS." . "Adaptive stochastic algorithms in non-linear regression"@en . "Adaptivn\u00ED stochastick\u00E9 algoritmy v neline\u00E1rn\u00ED regresi" . . . . . "Adaptive stochastic algorithms in non-linear regression"@en . . . . "Self-adaptive stochastic algorithms are applied to the estimation of parameters in non-linear regression models. Self-adaptive variant of controlled random search (CRS) is compared experimentally with novel self-adaptive variants of differential evolution (DE) using tasks of NIST reference datasets. Several variants of DE outperformed CRS in a few tasks only."@en . . . "1"^^ . "17310" . . "1"^^ . . "P(GA201/08/0472), Z(MSM6198898701)" . "J\u010CMF" . . . "301942" . . "RIV/61988987:17310/09:A1000QXQ" . "Adaptivn\u00ED stochastick\u00E9 algoritmy v neline\u00E1rn\u00ED regresi"@cs . . "Pribylina, SK" . "2008-09-08+02:00"^^ . "ROBUST 2008" . .