. "7" . . . "RIV/68407700:21110/07:01131834!RIV08-MSM-21110___" . "Computer Assisted Mechanics and Engineering Sciences" . . "411269" . . "Z(MSM6840770003)" . "2" . . "219;242" . "1232-308X" . . . "Back Analysis of Microplane Model Parameters Using Soft Computing Methods"@en . "Ku\u010Derov\u00E1, Anna" . "[A7C154E80A1B]" . . "RIV/68407700:21110/07:01131834" . . . . "Back Analysis of Microplane Model Parameters Using Soft Computing Methods" . "V tomto p\u0159\u00EDsp\u011Bvku je p\u0159edstavena metoda identifikace parametr\u016F mikroplo\u0161kov\u00E9ho modelu M4 pro beton zalo\u017Een\u00E1 na vrstven\u00FDch neuronov\u00FDch s\u00EDt\u00EDch."@cs . "Back Analysis of Microplane Model Parameters Using Soft Computing Methods"@en . "21110" . "24"^^ . "Lep\u0161, Mat\u011Bj" . . "PL - Polsk\u00E1 republika" . "Zeman, Jan" . . "A new procedure based on layered feed-forward neural networks for the microplane material model parameters identification is proposed in the present paper. Novelties are usage of the Latin Hypercube Sampling method for the generation of training sets, a systematic employment of stochastic sensitivity analysis and a genetic algorithm-based training of a neural network by an evolutionary algorithm. Advantages and disadvantages of this approach together with possible extensions are thoroughly discussed and analyzed." . "3"^^ . . "Global optimization; Inverse analysis; Layered neural networks; Microplane models; Stochastic sensitivity"@en . . "3"^^ . "Metoda identifikace parametr\u016F mikroplo\u0161kov\u00E9ho modelu zalo\u017Een\u00E1 na vrstven\u00FDch neuronov\u00FDch s\u00EDt\u00EDch"@cs . "A new procedure based on layered feed-forward neural networks for the microplane material model parameters identification is proposed in the present paper. Novelties are usage of the Latin Hypercube Sampling method for the generation of training sets, a systematic employment of stochastic sensitivity analysis and a genetic algorithm-based training of a neural network by an evolutionary algorithm. Advantages and disadvantages of this approach together with possible extensions are thoroughly discussed and analyzed."@en . . . "Metoda identifikace parametr\u016F mikroplo\u0161kov\u00E9ho modelu zalo\u017Een\u00E1 na vrstven\u00FDch neuronov\u00FDch s\u00EDt\u00EDch"@cs . . "Back Analysis of Microplane Model Parameters Using Soft Computing Methods" .