A modified multi-gene genetic programming approach for modelling true stress of dynamic strain aging regime of austenitic stainless steel 304

Meccanica

Volumn 49, Issue 5, 2014, Pages 1193-1209

  Garg, A ^a   Tai, K ^a   Gupta, A K ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE   (India)

Author keywords

Artificial neural network; Dynamic aging regime; Multi gene genetic programming; Support vector regression; True stress

Indexed keywords

AUSTENITIC STAINLESS STEEL; GENETIC PROGRAMMING; LEAST SQUARES APPROXIMATIONS; NEURAL NETWORKS; NUCLEAR REACTORS; REGRESSION ANALYSIS; TENSILE TESTING;

AUSTENITIC STAINLESS; DYNAMIC AGING; DYNAMIC STRAIN AGING; ELEVATED TEMPERATURE; LEAST SQUARES METHODS; MULTI-GENE GENETIC PROGRAMMING; SUPPORT VECTOR REGRESSION (SVR); TRUE STRESS;

GENES;

EID: 84899632962     PISSN: 00256455     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11012-013-9873-x     Document Type: Article

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